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Best Statistical Quality Control Project For Undergraduates students

Best Statistical Quality Control Project For Undergraduates students

Best Statistical Quality Control Project For Undergraduates students

 CHAPTER ONE

1.0 INTRODUCTION

No two products or characteristics are exactly same, because any process contains many sources of variability. In mass-manufacturing, traditionally, the quality of a finished article is ensured by post-manufacturing inspection of the product. Each article (or a sample of articles from a production lot) may be accepted or rejected according to how well it meets its design specifications. In contrast, SPC uses statistical tools to observe the performance of the production process in order to detect significant variations before they result in the production of a sub-standard article.

Most processes have many sources of variation; most of them are minor and may be ignored. If the dominant sources of variation are identified, however, resources for change can be focused on them. If the dominant assignable sources of variation is detected, potentially they can be identified and removed. Once removed, the process is said to be "stable". When a process is stable, its variation should remain within a known set of limits. That is, at least, until another assignable source of variation occurs. That is where the idea of statistical quality control arises as Quality control is a process by which entities review the quality of all factors involved in production. Quality control emphasizes testing of products to uncover defects and reporting to management who make the decision to allow or deny product release, whereas quality assurance attempts to improve and stabilize production (and associated processes) to avoid, or at least minimize, issues which led to the defect(s) in the first place.

BACKGROUND OF THE STUDY

NPK Fertilizer is an organic material of natural or synthetic origin (other than liming materials) that is added to soil to supply one or more plant nutrients essential to the growth of plants. It is primarily composed of three main elements: Nitrogen (N), Phosphorus (P), and Potassium (K), each of these being essential in plant nutrition. Among other benefits, Nitrogen helps plants grow quickly, while also increasing the production of seed and fruit, and bettering the quality of leaf and forage crops. Nitrogen is also a component of chlorophyll, the substance that gives plants their green color, and also aids in photosynthesis.

Phosphorus, also a key player in the photosynthesis process, plays a vital role in a variety of the things needed by plants. Phosphorus supports the formation of oils, sugars, and starches. The transformation of solar energy into chemical energy is also aided by phosphorus, as well as is development of the plant, and the ability to withstand stress. Additionally, phosphorus encourages the growth of roots, and promotes blooming.

Potassium, the third essential nutrient plants demand, assists in photosynthesis, fruit quality, the building of protein, and the reduction of disease. 

While these three elements only scratch the surface of healthy plant nutrition and growth, they are the main nutrients required in the development of healthy, productive plants.

German scientist Justus Von Liebig was responsible for the theory that nitrogen, phosphorous, and potassium levels are the basis for determining healthy plant growth.However, this theory, which dates to the 1800s, doesn't take into account the dozens of other nutrients and elements that are essential to plant growth such as sulfur, hydrogen, oxygen, carbon, magnesium, etc. Nor does the theory include the importance of beneficial soil organisms that helps plants flourish and fight off pests and diseases. Additional nutrients such as carbon, hydrogen, oxygen, sulfur, magnesium, copper, cobalt, sodium, boron, molybdenum, and zinc are just as important to plant development as N-P-K. Unfortunately, Justus Von Liebig's theory has developed into the dominant formula by which we nurish our edible and ornamental plants, lawns and trees.

Management of soil fertility has been the pre-occupation of farmers for thousands of years. The start of the modern science of plant nutrition dates to the 19th century and the work of German chemist Justus von Liebig, among others.

John Bennet Lawes, an English entrepreneur, began to experiment on the effects of various manures on plants growing in pots in 1837, and a year or two later the experiments were extended to crops in the field. One immediate consequence was that in 1842 he patented a manure formed by treating phosphates with sulphuric acid, and thus was the first to create the artificial manure industry. In the succeeding year he enlisted the services of Joseph Henry Gilbert, with whom he carried on for more than half a century on experiments in raising crops at the Rothamsted Experimental Station. The Birkeland–Eyde process was one of the competing industrial processes in the beginning of nitrogen based fertilizer production. It was developed by Norwegian industrialist and scientist Kristian Birkeland along with his business partner Sam Eyde in 1903, based on a method used by Henry Cavendish in 1784. This process was used to fix atmospheric nitrogen (N2) into nitric acid (HNO3), one of several chemical processes generally referred to as nitrogen fixation. The resultant nitric acid was then used as a source of nitrate (NO3-) in the reaction HNO3 → H+ + NO3-  which may take place in the presence of water or another proton acceptor. Nitrate is an ion which plants can absorb.

A factory based on the process was built in Rjukan and Notodden in Norway, combined with the building of large hydroelectric power facilities. 

The Birkeland-Eyde process is relatively inefficient in terms of energy consumption. Therefore, in the 1910s and 1920s, it was gradually replaced in Norway by a combination of the Haber process and the Ostwald process. The Haber process produces ammonia (NH3) from methane (CH4) gas and molecular nitrogen (N2). The ammonia from the Haber process is then converted into nitric acid (HNO3) in the Ostwald process.

Chemical fertilizers can have any number of additional ingredients including dirt, sand, and even materials that are potentially hazardous to your health and to the environment. Chemical fertilizer fillers are needed so that the nutrients aren't so concentrated that they will "burn" your plants, your skin, and anything else they touch. Organic fertilizers don't necessarily contain fillers, because they are made up of a variety of natural components that in one way or another benefit your plants.

Chemical fertilizers differ from organic fertilizers in the rate at which the nutrients become available to the plant. For example, the type of nitrogen typically found in chemical fertilizers dissolves very quickly in water. This means that excess nitrogen may find its way into groundwater and freshwater sources and contaminate the water. Additionally, many chemical fertilizers are now using phosphoric acid to create a high phosphorous content quickly and cheaply. According to research, this kind of phosphorous essentially neutralizes other important trace minerals from the soil that your plants need. 

Although organic and natural fertilizers usually have a lower N-P-K number, they are considered soil amendments that work slowly over time to improve your soil and to help your plants grow strong. They avoid the fast growth and flowering provided by chemical fertilizers that can actually weaken plants. High N-P-K numbers don't necessarily mean a better fertilizer.



STATEMENT OF THE PROBLEM 

Monitoring and controlling the process ensures that it operates at its full potential. At its full potential, the process can make as much conforming product as possible with a minimum (if not an elimination) of waste (rework or Scrap).  Likewise statistical quality control can be applied to any process  like production of N-P-K organic fertilizer where the "conforming product" (product meeting specifications) output can be measured.


AIMS AND OBJECTIVES

i. To determined the production process variation, by showing graphycally how one production level varies from the other.

ii. To reveal unwanted variation in quickly as would be detected with continous samplinh techniques of quality control


SIGNIFICANCE OF THE STUDY 

This research work will be of greater help in discovering the application of statistical quality control, process control in production process to upcoming researchers.  

The study of this kind has managerial relevance because there are inherent advantages in some of the discussions and conclusions that will be reached in this work. A logical extension of this research will enable government and companies especially in the manufactoring to understand the dynamics and ramifications of industrial changes in production and specification. This will enable them to plan on how to resolve it when they arise and how to improve the productivity of the N-P-K organic fertilizer. 


SCOPE AND LIMITATION 

This research work is restricted to the data obtain from quality control department of industrial minerals limited company, IBB way katsina, katsina state. Over the period of six years i.e. (2006 - 2011).

DEFINITIONS OF TERMS

Accuracy of measurements refers to the closeness of agreement between observed values and a known reference standard. Any offset from the known standard is called bias.

Attribute data Qualitative data that can be counted for recording and analysis. 

Cause-and-Effect Diagram A quality control tool used to analyze potential causes of problems in a product or process. It is also called a fishbone diagram or an Ishikawa diagram after its developer.

c-Chart A control chart based on counting the number of defects per constant size subgroup. Also known as a Count of Nonconformities chart. The c-chart is based on the Poisson distribution.

Center Line (CL) The line on the control chart that represents the long-run expected or average value of the quality characteristic that corresponds to the in-control state which occurs when only chance causes are present.

Control Chart A graphical mechanism for deciding whether the underlying process has changed based on sample data from the process. Control charts help determine which causes are “special” and thus should be investigated for possible correction. Control charts contain the plotted values of some statistical measure for a series of samples or subgroups, along with the upper and lower control limits for the process.

Control Limits Statistically calculated control chart lines which indicate how the process is behaving and whether the process is in control. There is typically an upper control limit (UCL) and a lower control limit (LCL). If the process is in control and only common causes are present, nearly all of the sample points fall within the control limits. Sometimes called the Natural Process Limits for the sample size.

CUSUM A control chart designed to detect small process shifts by looking at the Cumulative SUMs of the deviations of successive samples from a target value.

EWMA charts An Exponentially Weighted Moving Average control chart that uses current and historical data to detect small changes in the process. Typically, the most recent data is given the most weight, and progressively smaller weights are given to older data.

In-Control Process A process in which the quality characteristic being evaluated is in a state of statistical control. This means that the variation among the observed samples can all be attributed to common causes, and that no special causes are influencing the process.

Individuals Chart A control chart for processes in which individual measurements of the process are plotted for analysis. Also called an I-chart or X-chart.

Mean A measure of the location or center of data. Also called the average. The mean is calculated by summing all of the observations and dividing by the number of observations.

Median The “middle” value of a group of observations, or the average of the two middle values. The median is denoted by x tilde ( X% ).

Mixing A generally improper sampling technique that arises in practice when the output from several processes is first thoroughly mixed and then random samples are drawn from the mixture. This may increase the sample variability and make the control chart less sensitive to process changes. This action violates the fundamental rule of rational sampling.

Mode The observation that occurs most frequently in a sample. The data can have no mode, be unimodal, bimodal, etc.

Moving Range A measure used to help calculate the variance of a population based on differences in consecutive data. Two consecutive individual data values are compared and the absolute value of their difference is recorded on the moving range chart. The moving range chart is typically used with an Individuals (X) chart for single measurements.

Nonconforming Unit A unit with one or more nonconformities or defects. Also called a reject.

Nonconformity A specified requirement that is not fulfilled, such as a blemish, defect or imperfection.

np-Chart A control chart based on counting the number of defective units in each constant size subgroup. The np-chart is based on the binomial distribution.

Pareto Chart A problem-solving tool that involves ranking all potential problem areas or sources of variation according to their contribution to cost or total variation. Typically, 80% of the effects come from 20% of the possible causes, so efforts are best spent on these “vital few” causes, temporarily ignoring the “trivial many” causes.

p-Chart A control chart based on the proportion of nonconforming units per subgroup.

Process Capability A measure of the ability of a process to produce output that meets the process specifications.

R-Chart A control chart based on the range (R) of a subgroup, typically used in conjunction with an x-bar chart.

S-Chart A control chart based on the standard deviation, s, of a subgroup. The s-chart is typically used in conjunction with an x-bar chart.

Sample A subset of data from a population that can be analyzed to make inferences about the entire population.

Special Causes. A Causes of variation which arise periodically in a somewhat unpredictable fashion.

Stability A process is considered stable if it is free from the influences of special causes.

Standard Deviation A measure of the spread of a set of data from its mean, abbreviated:

Statistical Process Control (SPC) A collection of problem solving tools useful in achieving process stability and improving capability through the reduction of variability. SPC includes using control charts to analyze a process to identify appropriate actions that can be taken to achieve and maintain a state of statistical control and to improve the capability of the process.

Statistical Quality Control (SQC) Another name commonly used to describe statistical process control techniques.

Variables Data are Data values which are measurements of some quality or characteristic of the process. The data values are used to construct the control charts. 

Variation The differences among individual results or output of a machine or process.

X-Chart A control chart used for process in which individual measurements of the process are plotted for analysis. Also called an individuals chart or I-chart.











CHAPTER TWO

2.0  LITERATURE REVIEW

In this Chapter a review of literature on previous researches would be discussed. 

2.1 REVIEW OF PREVIOUS STUDIES

A study was conducted on The effect of different combinations of NPK and organic fertilizers on maize was studied by Sharma and Gupta (1998). They reported that the integration of 75% N through chemical fertilizer + 25% N through organic sources gave equal yield of 100% NPK. They further concluded that the organic manures increase the available N and P and water holding capacity of the soil. 

LAI and grain yield are positively correlated with each other (Shortall and Liebhardt, 1975). More availability of N&P delay the tasseling period (Farooqi, 1999). Due to the application of PM and mineral fertilizer, plants received large amount of nutrients throughout their growth period and nourished properly which resulted in maximum number of cobs per plant and number of grains per cob (Shah and Arif, 2000). More cob length and cob diameter with the application of urea and PM was also responsible for increasing the 1000-grian weight and seed yield of the maize crop. With the same treatment (50% N from urea and 50% N from PM). Higher harvest index value reflects the higher efficiency of converting dry matter into economic yield (Khaliq et al., 2004).

Integrated use of FYM and inorganic fertilizers (NPK+Zn) significantly increased maize grain yield by 89 % over NPK fertilizer alone (Adhikari et al., 2001).

Further, NPK fertilizer + compost produced three times higher maize biomass than obtained in non-treated plots. Combined application of organic and chemical fertilizers as 25 % N from FYM and 75 % N from urea produced significantly higher wheat grain and straw yield than 0:100 and 50:50 combination of N from FYM and urea (Mahmood-ul-Hassan et al., 1989).

Application of 30 t FYM ha-1 + 270 kg N ha-1 chemical fertilizer to potatoes produced statistically significant and positive cumulative effects (Roy et al., 2001). It was result of organic matter increase in soil that sustains productivity in the long-term. Similar results for yield increase of maize and wheat with synergistic use of organic matter and mineral N fertilizers were obtained by Thind et al. (2002) and Mugwe et al. (2009). 

Ahmad et al. (2002) observed that plant height and leaf area of wheat significantly increased by combining organic and inorganic N fertilizers. Hati et al. (2006) observed that using 10 t FYM + NPK in soybean for three years improved the seed yield (103 %), water-use efficiency (76 %), and root length density (70.5 %) as compared to control.

Low rate of NP fertilizer integrated with 5 t ha-1 organic manure is the most economical practice for maize (Negassa et al., 2001). Use of FYM + Effective Microorganisms (EM) + ½ rate of NPK fertilizer produced seed cotton yield similar to that with full rate NPK, enhanced NPK content in plants, and saved 85 kg N ha-1 (Khaliq et al., 2006). In rain-fed sub-mountainous region of India, wheat and maize yields increased significantly with soil and nutrient management practice (75 % NPK + FYM 10 t ha-1) over the farmer practice under wheat-maize cropping system (Hadda and Arora, 2006). Sheeba and Chellamuthu (2000) observed that application of 100 % NPK + FYM enhanced the grain yield of maize.

Yadav et al. (2006) observed that combined use of N, FYM and Zn proved the best in term of maize grain and stover yield, nutrient uptake, gross return, net return and benefit cost ratio against their sole application and farmers’ practice. Economic analysis suggested that use of ½ mineral N + ½ organic N along with EM in maize had better performance (Ahmed et al., 1999). Application of chemical fertilizer + 15 t FYM ha-1 to sugarcane produced the highest leaf area index, chlorophyll content, cane yield, and sugar content (Bokhtiar and Sakurai, 2005).

 Macro- and micro-nutrient concentrations in leaves were greater with organic manure than with mineral fertilizer. Continuous application of  FYM enhanced crop growth and increases root biomass (Naeem et al., 2009).

Nitrogen is the most critical element of plant growth. Different experiments conducted in this regard revealed that plant growth is affected differently by various N sources (Ryle et al., 1978). A comparison of different N sources elaborated that seedling growth (fresh or dry weight) was always more in nitrate than various reduced N sources (Lahav et al., 1976). However, urea was better than other reduced N sources (glutamine, ammonium malate and other ammonium salts). Similarly in another study, urea appeared to be the best source which gave the highest dry matter yield when four N sources (NH4 +, NH4OH, NO3 - and urea) were compared (De Mooy et al., 1973). 

Urea is a dominant source in many areas of the tropics. It has some obvious advantages in terms of high analysis, relatively low raw material cost, non-explosive character and lower acidifying properties than ammonium sulfate (Engelstand & Russell, 1975). Farmers and agricultural scientists have found that the response of crops to N in urea compares favorably with the N in NH4OH (Lahav et al., 1976). Urea converts rapidly to ammonia in the soil by hydrolysis. If applied properly, urea is as effective as other N sources in common use. Volatilization losses occurring from top dressing of most ammonium-N sources were much greater with heavier rates of urea used as top dressing while incorporation of urea in the soil reduced volatilization losses (Engelstand & Russell, 1975).

If farmers were to stop using fertilizer now, crop yields would drop during some years to a very low level, and many countries which today use substantial amounts of fertilizers and export agricultural produce would barely be in a position to feed their own population (Mengel, 1992). The demand for fertilizer N cannot be completely covered by rotation with N-fixing leguminous species (such as clover or soybean) unless more than 40% of arable land was cultivated with leguminous species; thus, a substantial proportion of arable land would be used for the production of fertilizer N. This would be more expensive than industrial production (Mengel, 1992). 

Sources of N, input for crop production including soil transformations and related plant conversions for crops that do and do not possess associative N-fixing systems (Hardy et al, 1975).

Cereal crops are influenced by N fertilizer in many ways. Firstly, it increases N supply and helps/results in an increase in the duration and size of the crop canopy (Leaf area index, LAI and Leaf area duration, LAD), which ultimately results in high rates of dry matter production (Muchow and Davis, 1988). Secondly the time of application of N and its amounts can also affect the growth of the plants. Thirdly, late and or heavy N application may result in grain unacceptable to industry because of their high N/protein contents. Therefore, in this way quality and quantity of grain is also influenced by fertilizer application. Finally, lodging/attack of various pathogens are commonly observed in the crops, which are heavily fertilized. It was also found that deficiency or excess of N affects the partitioning of assimilates between vegetative and reproductive organs (Donald and Hamblin, 1976). By increasing N supply, leaf area index, leaf area duration, photosynthetic radiation interception and radiation use efficiency also increase (Muchow and Davis, 1988; Sinclair and Horic, 1989 and Connor et al., 1993).


2.2 DESCRIPTION OF PRODUCTION PROCESSES

 Process Description

NPK Fertilizers industry is a form of secondary chemical production. It is necessary to understand the inputs and processing techniques in order to identify the pollution sources and abatement measures in this industry. 

Fertilizers may be categorized into two groups, natural and synthetic fertilizers. Synthetic fertilizers include different types according to their chemical composition, physical status and solubility in water. According to their chemical composition, fertilizers are categorized into three main groups as follows:

Phosphatic fertilizers containing phosphorous as a base element, which is expressed by P2O5 %.

Nitrogen fertilizers containing nitrogen as a base element, which is expressed by N2 %.

Potassium fertilizers containing potassium as a base element, which is expressed by K2O %.

In addition to the straight fertilizers containing single nutrient (N or P or K) there are the complex fertilizers containing two or three major plant nutrient N.P.K. Enormous varieties of NPK according to their contents of nutrients are available. Liquid fertilizers are also produced worldwide including hundreds of forms containing one or more nutrients together with trace elements. NPK fertilizers are produced in the Egyptian Fertilizer Development Center Pilot Plant, located in Talkha, in limited quantities according to the clients requests. Also in Delta Company there is a plant that produces urea- ammonium nitrate liquid fertilizer (32 % N). 

The liquid fertilizers must be free of solids to avoid clogging the slurry nozzles. Concentrated phosphoric acid is used as the basis for high analysis liquids. When reacted with ammonia, it gives neutral solution which does not crystallize at low temperature. If ammoniated under pressure, ammonium polyphosphate forms. This can be stored and shipped as a solid and dissolved readily when needed. Another liquid fertilizer is anhydrous ammonia, vaporized to a gas and ploughed directly into the soil. A combination of solution/ suspension containing 13 % nitrogen and 43 % P2O5 has been developed, to which custom blenders can add potash and trace elements if needed. 


Raw Materials, Chemicals and Other Inputs

Main Raw Materials

Inputs for the fertilizers industry vary according to the fertilizer type. Phosphate rock is considered the main raw material for the production of phosphate type fertilizers, while in case of nitrogenous fertilizers, ammonia is considered the main raw material. Ammonia is produced by synthesis of nitrogen and hydrogen. The latter is generated either by steam reforming of natural gas, or electrolysis of water. On the other hand, nitrogen is produced either from air liquification as in KIMA- Aswan, or combustion of natural gas. 

Large quantities of acids are also used, namely sulphuric acid, nitric acid and phosphoric acid. In all fertilizers plants those acids are produced on-site. Raw sulphur is considered the main raw material for the production of sulphuric acid, whereas phosphoric acid production depends on phosphate rock as raw material. The production of nitric acid is based on the on-site produced ammonia.

The involved catalysts in the fertilizers industry are as follows:

In ammonia production: 

CoO, MoO3 and ZnO for sulphur removal.

NiO for primary and secondary reformers.

Iron oxide and chromium for CO high shift conversion and copper oxide and zinc oxide for low shift.

NiO catalyst for methanation.

Iron promoted catalyst for ammonia synthesis. 

In nitric acid production: platinum/ rhodium catalyst.

In sulphuric acid production: vanadium pentoxide catalyst.

These catalysts are usually not considered as inputs, instead they are considered part of the equipments. This is related to nature of the reactors in this industry, which are fixed bed reactors. Hence the catalysts only aid the reaction, without reacting themselves. According to several factors, catalysts loose their activity after long operating hours which defer from a catalyst to another. Consequently, they need to be regenerated, usually on-site except for the very expensive catalysts such as platinum alloy catalyst which is regenerated in the manufacturing company.

Solvents, carbon dioxide, ground dolomite as coating materials and limestone are also used in fertilizers industry. Table (2) summarizes the major inputs according to the involved operation.

Other Inputs (Water, Fuel and Steam)

Large quantities of water are consumed for several purposes involving cooling, process, steam generating, floor washing and cleaning,  ..etc. Steam is generated in these plants in huge quantities for heating, reforming, stripping and other purposes. This steam is generated in boilers by fuel combustion. The fuel type differs from one facility to the other, including fuel oil (mazot), gas oil (solar) or natural gas. Fuel oil (mazot) is widely used due to its cheap cost. On burning, it generates on burning air emissions with high concentration of sulphur oxides (SOx). 

Fertilizers industry is considered one of the complex chemical sector, which includes several production lines and service units.

Phosphates Fertilizers

Phosphate fertilizers industry is considered one of the most polluting industries in Egypt. No modernization or pollution abatement plans and technologies were set for this industry, in spite of the implementation of such technologies world wide. It is worth mentioning that the production of phosphate fertilizers in Egypt is limited (installed capacities 1.2 millions tons 15.5 P2O5) compared with nitrogenous fertilizers (installed capacities 12 million tons estimated as 15 % N2).

The various phosphate fertilizers, depending on their composition, have different solubility in soil solutions and are, therefore, assimilated by plants differently. Phosphate fertilizers include single superphosphate and triple superphosphate. The single superphosphate is a mixture of monocalcium phosphate and gypsum (available P2O5 almost 16- 22 %), while triple-superphosphate is composed mainly of monocalcium phosphate (available P2O5) almost 46 %).

a) Single supephosphate Fertilizer

The manufacturing process depends on reacting phosphate rock with sulfuric acid and the fertilizer contains about (16- 20 %) P2O5. The net reaction proceeds as follows:


Ca F2. 3Ca3 (PO4)2 + 7H2SO4 + 14H2O →   3Ca(H2PO4)2 + 7Ca SO4 . 2H2O + 2HF


The process can be divided into two stages as follows:

The first stage represents the  diffusion of sulfuric acid to the rock particles accompanied by a rapid chemical reaction on the particle surface, which continues until the acid is completely consumed, and crystallization of calcium sulphate.

The second stage represents the diffusion of the formed phosphoric acid into the pores of the rock particles which did not decompose. This stage is accompanied by a second reaction.  


In this process ground phosphate rock is transported from the storage site to automatic weight, by a system of belt and screw conveyors and elevators, which feed the continuous action double conical mixer. The sulfuric acid is continuously diluted with water in a batch mixer to a 75 % concentration, then fed to the mixer to react with ground phosphate rock where a first reaction takes place. This reaction ends in the reaction mixer in 30- 60 minutes, during the period of settling and hardening of the superphosphate slurry, which is caused by the relatively rapid crystallization of the low solubility calcium sulphate. The next stage of the process is ageing of the superphosphate, i.e. the formation and crystallization of monocalcium phosphate in the den. The formed slurry is transported to the continuous-action reaction den which has a very low travel speed to allow for solidifying (see fig 2), where formation of superphosphate takes place (settling and hardening of the slurry in the first stage of ageing). Considerable quantities of fluoride compounds are evolved from the acidulation, they are sent to the scrubbers shown in fig (3). 


The superphosphate powder, from the den, is transferred for ageing by a belt conveyor, located below the den, to the pile storage for curing, or completion of chemical reaction, which takes 2-6 weeks to a P2O5 availability acceptable for plant nutrient. The raw fertilizer is uniformly distributed by a scattering device and in order to accelerate the ageing operation, the superphosphate is agitated during storage by means of a grab-bucket crane. The end product still contains a certain amount of uncombined phosphoric acid, which makes the fertilizer more hygroscopic. Neutralizing admixtures are used to remove the free acid of the superphosphate, or it is treated with gaseous ammonia. These procedures improve the physical properties of the superphosphate. They lower the moisture content, the hygroscopic  and the tendency to cake.  If ammonia treatment is used, an additional nutritional component (N2) is also introduced into the fertilizer.

 

During reaction of the phosphate with sulphuric acid in the den, hydrogen fluoride evolves and reacts with the silica contained in the phosphates and forms gaseous silicon-tetrafluoride (SiF4) and fluo slicic acid (H2SiF6). The continuous den is, therefore, enclosed so that fumes of these compounds do not escape into the working place. The fluorous gases, containing H2SiF6 vapors, are withdrawn through an opening in the den roof into a ventilation pipe to an absorption unit and are utilized for making sodium fluo silicates.


Superphosphate is granulated in drum granulators to improve its physical properties. In the granulator, the superphosphate powder (after being cured for 2-6 weeks) is wetted with water fed into the drum through nozzles, and rolled into granules of different size which are then dried, screened into size fractions cooled and the product is bagged in plastic (polyethylene) bags. The over size granules are ground and recycled, with the undersize granules, to the den. 

b) Triple Superphosphate Fertilizer

Fig (4) shows the block flow diagram for manufacturing of triple superphosphate. This type of fertilizers is much more concentrated than the ordinary superphosphate, containing 45- 46 % of available P2O5. Triple superphosphate is manufactured by the action of phosphoric acid on phosphate rock. The main reaction is: 


CaF2. 3Ca3 (PO4)2 + 14H3PO4 →  10Ca (H2PO4)2 + 2HF ↑

(Phosphate Rock)                                   (Triple Superphosphate)


A process similar to single superphosphate production is used, in which pulverized phosphate rock is mixed with phosphoric acid in a two-stage reactor. The resultant slurry is sprayed into the granulator. The slurry is sprayed into the drum granulation co-current with flue gases of fired fuel (natural gas or fuel oil and air). The product is screened and off-size is recycled back to the granulator. The on-size product is cooled and stored ready for being bagged. The exhaust gases from the reactor, granulator and cooler are scrubbed to remove fluoric compounds.


c) Wet Process of Phosphoric Acid Production

The main process for producing phosphoric acid is by the use of sulfuric acid as shown in fig (5). The major reaction is 


Ca F2. 3Ca3 (PO4)2 + 10H2SO4 + 20H2O →  10CaSO4. 2H2O + 2HF + 6H3PO4 (Phosphate Rock)  (Gypsum)


Raw phosphate rock, ground to less than 200 mesh size, is fed to a chute where a recycled stream of weak H3PO4 washes it into a reaction tank and digested with strong sulfuric acid. The retention time ranges from 1.5 to 12 hr, and conditions are controlled to produce gypsum crystals that are readily filterable. Considerable heat is generated in the reactor when the sulphuric acid and phosphate rock react. In older plants, this heat was removed by blowing air over the hot slurry surface. In modern plants, a portion of the slurry is cooled by vacuum flash, and then recycled back into the reactor. The reaction mixture is filtered using a tilting-pan filter. The feed to the filter continuously enters the pans, which are connected to the vacuum source. The circular frame supporting the pans rotates so that each pan is moved successively under the desired number of washes. After the final wash liquor has completely drained off, the vacuum is released and the pan is inverted a full 180 degree. The cake drops off, its removal is being ensured by a reverse blast of air through the filter medium, which is then scoured fresh and cleaned by a high-pressure shower while the pan is still inverted. The filter medium and drainage area are then purged by vacuum, and the pan returned to the feed position. 


This process produces 28 to 32 % acid which must be concentrated in an evaporator, to meet phosphate feed material specifications for fertilizer production. The crude acid is often black and contains dissolved metals and fluorine, and dissolved and colloidal organic compounds. Suspended solid impurities are usually removed by settling. Solvent extraction or solvent precipitation is used to remove the dissolved impurities. Solvent extraction uses a partially miscible solvent, such as n-butanol, iso-butanol, or n-heptanol. The phosphoric acid is extracted and the impurities are left behind. Back-extraction with water recovers the purified phosphoric acid. Solvent precipitation uses a completely miscible solvent plus alkalis or ammonia to precipitate the impurities as phosphate salts. After filtration, the solvent is separated by distillation and recycled.


Major Hazards

The chance of an acid spill from storage tanks is very small, with the highest risk being a leak from the tank because of corrosion. Corrosion with Phosphoric acid is a relatively slow process and starts with a small hole in the tank. Normally the leak will be seen and the tank emptied before a significant spillage can take place. There is also a risk of the loading pipe cracking during acid loading and this could lead to a significant uncontrolled spillage. Pumping equipment should be available for emptying the pipes.


The more important considerations in the design and construction of phosphogypsum disposal areas are: site selection, cooling ponds and percolation control. The height of the stack depends on the engineering properties of the underlying soil and its load bearing strength, if there are no legal restrictions. The cooling pond surfaces will have to be adapted to local climatic conditions and the water balance in the plant. The process water associated with phosphogypsum is highly acidic and contains high levels of contaminants. some of the following options may be necessary to prevent this water reaching the surrounding ground water system: seepage collection ditches, intercept wells, natural barriers, lining systems (natural or synthetic) and fixing of soluble P2O5 and trace elements by neutralization.

d) Sulphuric Acid Manufacturing Process:

The process used is the contact process. Fig (6) presents the raw materials, products and expected pollutants. Elemental sulphur is usually used as raw material and is oxidized to sulphur dioxide. The sulphur dioxide is then oxidized to sulphur trioxide using vanadium pentoxide catalyst. The chemical reactions taking place can be expressed by the following equations:


S + O2                       →         SO2


SO2 +  ½  O2     SO3


SO3 + H2O   →          H2SO4


Sulphur is first melted using steam and then filtered to remove contaminants that might poison the catalyst. The liquid sulphur is burned in a sulphur burner using filtered dry air. The air has been dried using concentrated sulphuric acid. The result of burning is a gas containing 8-11% SO2 and 8-13% oxygen, which is cooled in a waste-heat boiler to about 420º C. The specific inlet temperature of the gas entering the converter is dependent upon the quantity and quality of the catalyst and the composition and flow rate of the sulphur dioxide gas, but it is usually in excess of 426ºC. The converter contains layers of catalyst, usually vanadium pentoxide, placed in horizontal trays or beds arranged so that the gas containing SO2 and an excess of oxygen passes through two, three, or four stages of catalyst. As the gas passes through the converter, approximately 95 to 98% of SO2 is converted to sulphur trioxide, with the evolution of considerable heat. Maximum conversion cannot be obtained if the temperature in any stage becomes too high. Therefore, gas coolers are employed between converter stages. The concentration of sulphur trioxide leaving the converter at 426 - 454ºC is approximately the same as that of the entering sulphur dioxide. 


The converter gas is cooled to 232 to 260º C in an economizer or tubular heat exchanger. The cooled gas enters the absorption tower where the sulphur trioxide is absorbed with high efficiency in circulating stream of 98 to 99 % sulphuric acid. The sulphur trioxide combines with the excess water in the acid to form more concentrated sulphuric acid. To reduce the emission of sulphur trioxide in the exit gases, a second absorption stage is added where exit gases from the absorption tower are introduced into a second absorption tower. The gases leaving the absorbing tower may pass through a heat exchanger, in which they are reheated to about 426º C before reentering the converter. They are then passed through the catalyst, cooled, and flow through the absorption towers and then to the atmosphere. A great source of pollution in this process is due to the emission of SO2, SO3 gas with acid mist from the absorption tower and leaks from heat exchangers. 

Major Hazards

The highest risk hazard for accidental pollution exists during the storage and transportation of the sulphuric acid. Plants have different systems to collect leaks and spillages.  Gas leaks are not normally a problem as they are handled by various monitoring and control systems, which measure the SO2 content in air.



CHAPTER THREE

3.0 INTRODUCTION

This section discusses on how the data were collected to achieve the selected objective, therefore. In the pursuance of this task, the method of data collection employed for the purpose of this study will be discussed.

UNESCO (1954) defined methodology as “the systematic study of principles guiding scientific and philosophical investigation”.

There are two types of data, namely Primary and Secondary data

PRIMARY DATA: These are data’s that you collect your self-using methods such as interview and questionnaire the key point here is that the data you collect is unique to you and your researcher and no one else have access to it. While 

SECONDARY DATA: These are data’s collected by someone other than the user, common sources of secondary data for social sciences include censuses, organizational records and data collected through qualitative methodologies or qualitative research, All methods of data collection can supply quantitative data (numbers, statistics or financial) or qualitative data (usually words or text).quantitative data may often be presented in tabular or graphical form.

3.1 METHOD USED IN COLLECTION OF DATA

Due to the nature of this project it is only appropriate for us to make use of secondary data rather than carrying out a survey (primary data).

The data were obtained from the ...................................................... This department collected the primary data initially for an administrative purpose has it...................................

The productions where in metric tons, available publication was that of 2003 to 2012 i.e. 10 years.

3.2 STATISTICAL TOOLS USED

The following are the statistical tools used in this project paper

3.2.1 CONTROL CHARTS

Control charts, also known as Shewhart charts (after Walter A. Shewhart) or process-behavior charts, in statistical process control are tools used to determine if a manufacturing or business process is in a state of statistical control.

If analysis of the control chart indicates that the process is currently under control (i.e., is stable, with variation only coming from sources common to the process), then no corrections or changes to process control parameters are needed or desired. In addition, data from the process can be used to predict the future performance of the process. If the chart indicates that the monitored process is not in control, analysis of the chart can help determine the sources of variation, as this will result in degraded process performance. A process that is stable but operating outside of desired (specification) limits (e.g., scrap rates may be in statistical control but above desired limits) needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process. 

The control chart is one of the seven basic tools of quality control. Typically control charts are used for time-series data, though they can be used for data that have logical comparability (i.e. you want to compare samples that were taken all at the same time, or the performance of different individuals), however the type of chart used to do this requires consideration.

The control chart was invented by Walter A. Shewhart while working for Bell Labs in the 1920s.  The company's engineers had been seeking to improve the reliability of theirtelephony transmission systems. Because amplifiers and other equipment had to be buried underground, there was a stronger business need to reduce the frequency of failures and repairs. By 1920, the engineers had already realized the importance of reducing variation in a manufacturing process. Moreover, they had realized that continual process-adjustment in reaction to non-conformance actually increased variation and degraded quality. Shewhart framed the problem in terms of Common- and special-causes of variation and, on May 16, 1924, wrote an internal memo introducing the control chart as a tool for distinguishing between the two. Shewhart's boss, George Edwards, recalled: "Dr. Shewhart prepared a little memorandum only about a page in length. About a third of that page was given over to a simple diagram which we would all recognize today as a schematic control chart. That diagram, and the short text which preceded and followed it set forth all of the essential principles and considerations which are involved in what we know today as process quality control." Shewhart stressed that bringing a production process into a state of statistical control, where there is only common-cause variation, and keeping it in control, is necessary to predict future output and to manage a process economically.

Shewhart created the basis for the control chart and the concept of a state of statistical control by carefully designed experiments. While Shewhart drew from pure mathematical statistical theories, he understood data from physical processes typically produce a "normal distribution curve" (a Gaussian distribution, also commonly referred to as a "bell curve"). He discovered that observed variation in manufacturing data did not always behave the same way as data in nature (Brownian motion of particles). Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process, while others display uncontrolled variation that is not present in the process causal system at all times. 

In 1924 or 1925, Shewhart's innovation came to the attention of W. Edwards Deming, then working at the Hawthorne facility. Deming later worked at the United States Department of Agriculture and became the mathematical advisor to the United States Census Bureau. Over the next half a century, Deming became the foremost champion and proponent of Shewhart's work. After the defeat of Japan at the close of World War II, Deming served as statistical consultant to the Supreme Commander for the Allied Powers. His ensuing involvement in Japanese life, and long career as an industrial consultant there, spread Shewhart's thinking, and the use of the control chart, widely in Japanese manufacturing industry throughout the 1950s and 1960s.

3.2.3 STATISTICAL QUALITY CONTROL

Statistical quality control refers to the use of statistical methods in the monitoring and maintaining of the quality of products and services.  Statistical quality control may be categorized into two broad areas as one method, referred to as acceptance sampling, can be used when a decision must be made to accept or reject a group of parts or items based on the quality found in a sample. A second method, referred to as statistical process control.


3.2.4 STATISTICAL PROCESS CONTROL (SPC)

Statistical process control (SPC) is a method of quality control which uses statistical methods. SPC is applied in order to monitor and control a process. Monitoring and controlling the process ensures that it operates at its full potential. At its full potential, the process can make as much conforming product as possible with a minimum (if not an elimination) of waste (rework or Scrap). SPC can be applied to any process where the "conforming product" (product meeting specifications) output can be measured. Key tools used in SPC include control charts; a focus on continuous improvement; and the design of experiments. An example of a process where SPC is applied is manufacturing lines.

3.2.5 ACCEPTANCE SAMPLING

Acceptance sampling is the process of randomly inspecting a sample of goods and deciding whether to accept the entire lot based on the results. Acceptance sampling determines whether a batch of goods should be accepted or rejected.

Inspection provides a means for monitoring quality. For example, inspection may be performed on incoming raw material, to decide whether to keep it or return it to the vendor if the quality level is not what was agreed on. Similarly, inspection can also be done on finished goods before deciding whether to make the shipment to the customer or not. However, performing 100% inspection is generally not economical or practical, therefore, sampling is used instead.

Acceptance Sampling is therefore a method used to make a decision as to whether to accept or to reject lots based on inspection of sample(s). The objective is not to control or estimate the quality of lots, only to pass a judgment on lots.

Using sampling rather than 100% inspection of the lots brings some risks both to the consumer and to the producer, which are called the consumer's and the producer's risks, respectively. We encounter making decisions on sampling in our daily affairs.


3.3 CONCEPT OF VARIABILITY

Shewhart read the new statistical theories coming out of Britain, especially the work of "Student", Karl Pearson, and Ronald Fisher. However, he understood that data from physical processes seldom produced a "normal distribution curve"; that is, a Gaussian distribution or "bell curve". He discovered that data from measurements of variation in manufacturing did not always behave the way as data from measurements of natural phenomena (for example, Brownian motion of particles). Shewhart concluded that while every process displays variation, some processes display variation that is controlled and natural to the process ("common" sources of variation). Other processes display variation that is not controlled and that is not present in the causal system of the process at all times ("special" sources of variation). 

No two products or characteristics are exactly same, because any process contains many sources of variability. In mass-manufacturing, traditionally, the quality of a finished article is ensured by post-manufacturing inspection of the product. Each article (or a sample of articles from a production lot) may be accepted or rejected according to how well it meets its design specifications. In contrast, SPC uses statistical tools to observe the performance of the production process in order to detect significant variations before they result in the production of a sub-standard article. Any source of variation at any point of time in a process will fall into one of two classes.

i) "Common Causes" - sometimes referred to as nonassignable, normal sources of variation. It refers to many sources of variation that consistently acts on process. These types of causes produce a stable and repeatable distribution over time.

ii) "Special Causes" - sometimes referred to as assignable sources of variation. It refers to any factor causing variation that affects only some of the process output. They are often intermittent and unpredictable.

Most processes have many sources of variation; most of them are minor and may be ignored. If the dominant sources of variation are identified, however, resources for change can be focused on them. If the dominant assignable sources of variation is detected, potentially they can be identified and removed. Once removed, the process is said to be "stable". When a process is stable, its variation should remain within a known set of limits. That is, at least, until another assignable source of variation occurs. For example, a breakfast cereal packaging line may be designed to fill each cereal box with 500 grams of cereal. Some boxes will have slightly more than 500 grams, and some will have slightly less. When the package weights are measured, the data will demonstrate a distribution of net weights. If the production process, its inputs, or its environment (for example, the machines on the line) change, the distribution of the data will change. For example, as the cams and pulleys of the machinery wear, the cereal filling machine may put more than the specified amount of cereal into each box. Although this might benefit the customer, from the manufacturer's point of view, this is wasteful and increases the cost of production. If the manufacturer finds the change and its source in a timely manner, the change can be corrected (for example, the cams and pulleys replaced).

3.4 TYPES OF CONTROL CHARTS

3.4.1 THE  AND R CHART

The  And R chart is a type of control chart used to monitor variables data when samples are collected at regular intervals from a business or industrial process. 

The chart is advantageous in the following situations: 

The sample size is relatively small (say, n ≤ 10— and s charts are typically used for larger sample sizes)

The sample size is constant

Humans must perform the calculations for the chart

The "chart" actually consists of a pair of charts: One to monitor the process standard deviation (as approximated by the sample moving range) and another to monitor the process mean, as is done with the  and s and individuals control charts. The  and R chart plots the mean value for the quality characteristic across all units in the sample, , plus the range of the quality characteristic across all units in the sample as follows:

R = xmax - xmin.

The normal distribution is the basis for the charts and requires the following assumptions:

The quality characteristic to be monitored is adequately modeled by a normally distributed random variable

The parameters μ and σ for the random variable are the same for each unit and each unit is independent of its predecessors or successors

The inspection procedure is same for each sample and is carried out consistently from sample to sample

The control limits for this chart type are: 

 (lower) and  (upper) for monitoring the process variability

 for monitoring the process mean

where  and  are the estimates of the long-term process mean and range established during control-chart setup and A2, D3, and D4 are sample size-specific anti-biasing constants. The anti-biasing constants are typically found in the appendices of textbooks on statistical process control.

As with the  and s and individuals control charts, the  chart is only valid if the within-sample variability is constant. Thus, the R chart is examined before the  chart; if the R chart indicates the sample variability is in statistical control, then the chart is examined to determine if the sample mean is also in statistical control. If on the other hand, the sample variability isnot in statistical control, then the entire process is judged to be not in statistical control regardless of what the  chart indicates.

 3.4.2 THE  AND S CHART

 the  and s chart is a type of control chart used to monitor variables data when samples are collected at regular intervals from a business or industrial process.

The chart is advantageous in the following situations: 

The sample size is relatively large (say, n > 10— and R charts are typically used for smaller sample sizes)

The sample size is variable

Computers can be used to ease the burden of calculation

The "chart" actually consists of a pair of charts: One to monitor the process standard deviation and another to monitor the process mean, as is done with the  and R and individuals control charts. The  and s chart plots the mean value for the quality characteristic across all units in the sample, , plus the standard deviation of the quality characteristic across all units in the sample as follows:

.

The normal distribution is the basis for the charts and requires the following assumptions:

The quality characteristic to be monitored is adequately modeled by a normally-distributed random variable

The parameters μ and σ for the random variable are the same for each unit and each unit is independent of its predecessors or successors

The inspection procedure is same for each sample and is carried out consistently from sample to sample

The control limits for this chart type are: 

 (lower) and  (upper) for monitoring the process variability

 for monitoring the process mean

where  and  are the estimates of the long-term process mean and range established during control-chart setup and A3, B3, and B4 are sample size-specific anti-biasing constants. The anti-biasing constants are typically found in the appendices of textbooks on statistical process control.

As with the  and R and individuals control charts, the  chart is only valid if the within-sample variability is constant.[4] Thus, the s chart is examined before the  chart; if the s chart indicates the sample variability is in statistical control, then the  chart is examined to determine if the sample mean is also in statistical control. If on the other hand, the sample variability is not in statistical control, then the entire process is judged to be not in statistical control regardless of what the  chart indicates.

When samples collected from the process are of unequal sizes (arising from a mistake in collecting them, for example), there are two approaches.

3.4.3 THE P-CHART 

The P-Chart is a type of control chart used to monitor the proportion of nonconforming units in a sample, where the sample proportion nonconforming is defined as the ratio of the number of nonconforming units to the sample size, n. 

The p-chart only accommodates "pass"/"fail"-type inspection as determined by one or more go-no go gauges or tests, effectively applying the specifications to the data before they are plotted on the chart. Other types of control charts display the magnitude of the quality characteristic under study, making troubleshooting possible directly from those charts.

The control limits for this chart type are  where  is the estimate of the long-term process mean established during control-chart setup. Naturally, if the lower control limit is less than or equal to zero, process observations only need be plotted against the upper control limit. Note that observations of proportion nonconforming below a positive lower control limit are cause for concern as they are more frequently evidence of improperly calibrated test and inspection equipment or inadequately trained inspectors than of sustained quality improvement. 

Some organizations may elect to provide a standard value for p, effectively making it a target value for the proportion nonconforming. This may be useful when simple process adjustments can consistently move the process mean, but in general, this makes it more challenging to judge whether a process is fully out of control or merely off-target (but otherwise in control).


3.4.4 THE NP-CHART 

The Np-Chart is a type of control chart used to monitor the number of nonconforming units in a sample. It is an adaptation of the p-chart and used in situations where personnel find it easier to interpret process performance in terms of concrete numbers of units rather than the somewhat more abstract proportion. 

The np-chart differs from the p-chart in only the three following aspects:

The control limits are, where n is the sample size and  is the estimate of the long-term process mean established during control-chart setup.

The number nonconforming (np), rather than the fraction nonconforming (p), is plotted against the control limits.

The sample size, , is constant.





















CHAPTER FOUR

DATA ANALYSIS AND DISCUSSION OF RESULTS

4.1 INTRODUCTION

This chapter deals with the exploration of the collected data and the output of the analysis for the use of statistical quality control.

4.2 ANALYSIS, RESULTS AND DISCUSSION

The following are the output of the analyze data using statistical software for social sciences (SPSS).

X-BAR AND RANGE FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2009.

X-bar Chart

-----------

UCL: +3.0 sigma = 6562.01 Centerline = 5672.96

LCL: -3.0 sigma = 4783.91 0 beyond limits

Range Chart

-----------

UCL: +3.0 sigma = 1545.23   Centerline      = 472.747

LCL: -3.0 sigma = 0.0               0 beyond limits



X-BAR AND R-BAR DISCUSSION OF THE RESULTS FOR 2009

 This procedure creates X-Bar and R charts for number of NPK Organic Fertilizer in pieces and in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 5672.96 and a standard deviation equal to 419.102.  These parameters were estimated from the data.  Of the 3 nonexcluded points shown on the charts, 0 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 0 or more points beyond the limits just by chance is 1.0 if the data comes from the assumed distribution, we cannot reject the hypothesis that the process is in a state of statistical control at the 90% or higher confidence level

X-BAR AND S STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2009

X-bar Chart

-----------

UCL: +3.0 sigma = 6561.71 Centerline      = 5672.96

LCL: -3.0 sigma = 4784.21 0 beyond limits

S Chart

-------

UCL: +3.0 sigma = 1091.94

Centerline      = 334.282

LCL: -3.0 sigma = 0.0

0 beyond limits


X-BAR AND S CHART DISCUSSION OF THE RESULTS FOR 2009

This procedure creates X-Bar and S charts for number of NPK Organic Fertilizer in pieces and in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 5672.96 and a standard deviation equal to 418.961.  These parameters were estimated from the data.  Of the 3 nonexcluded points shown on the charts, 0 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 0 or more points beyond the limits just by chance is 1.0 if the data comes from the assumed distribution, we cannot reject the hypothesis that the process is in a state of statistical control at the 90% or higher confidence level.

X-BAR AND RANGE STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR  2010

Number of subgroups = 12  Subgroup size = 2.0  0 subgroups excluded

X-bar Chart

-----------

UCL: +3.0 sigma = 6215.79 Centerline      = 5240.43

LCL: -3.0 sigma = 4265.07 4 beyond limits

Range Chart

-----------

UCL: +3.0 sigma = 1695.24 Centerline      = 518.64

LCL: -3.0 sigma = 0.0 0 beyond limits



X-BAR AND R CHART DISCUSSION OF THE RESULTS FOR 2010

 This procedure creates X-Bar and R charts for number of NPK Organic Fertilizer in pieces and in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 5240.43 and a standard deviation equal to 459.787.  These parameters were estimated from the data.  Of the 12 nonexcluded points shown on the charts, 4 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 4 or more points beyond the limits just by chance is 0.0 if the data comes from the assumed distribution, we can declare the process to be out of control at the 99% confidence level.

X-BAR AND S STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2010

X-bar Chart

-----------

UCL: +3.0 sigma = 6215.46 Centerline      = 5240.43

LCL: -3.0 sigma = 4265.4 4 beyond limits

S Chart

-------

UCL: +3.0 sigma = 1197.95 Centerline      = 366.734

LCL: -3.0 sigma = 0.0 0 beyond limits


DISCUSSION OF THE RESULTS FOR X-BAR AND S CHART 2010

This procedure creates X-Bar and S charts for number of NPK Organic Fertilizer in pieces for year and number of NPK Organic Fertilizer in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 5240.43 and a standard deviation equal to 459.633.  These parameters were estimated from the data.  Of the 12 nonexcluded points shown on the charts, 4 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 4 or more points beyond the

limits just by chance is 0.0 if the data comes from the assumed distribution, we can declare the process to be out of control at the 99% confidence level.


X-BAR AND RANGE STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2011

X-bar Chart

-----------

UCL: +3.0 sigma = 7229.19 Centerline      = 5317.77

LCL: -3.0 sigma = 3406.34 0 beyond limits

Range Chart

-----------

UCL: +3.0 sigma = 3322.19 Centerline      = 1016.39

LCL: -3.0 sigma = 0.0 0 beyond limits


X-BAR AND R CHART DISCUSSION OF THE RESULTS FOR 2011

  This procedure creates X-Bar and R charts for number of NPK Organic Fertilizer in pieces for year and number of NPK Organic Fertilizer in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 5317.77 and a standard deviation equal to 901.055.  These parameters were estimated from the data.  Of the 12 nonexcluded points shown on the charts, 0 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 0 or more points beyond the limits just by chance is 1.0 if the data comes from the assumed distribution, we cannot reject the hypothesis that the process is in a state of statistical control at the 90% or higher confidence level.

X-BAR AND S STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2011

X-bar Chart

-----------

UCL: +3.0 sigma = 7228.55 Centerline      = 5317.77

LCL: -3.0 sigma = 3406.98 0 beyond limits

S Chart

-------

UCL: +3.0 sigma = 2347.64 Centerline      = 718.696

LCL: -3.0 sigma = 0.0 0 beyond limits


X-BAR AND S CHART DISCUSSION OF THE RESULTS FOR 2011

   This procedure creates X-Bar and S charts for number of NPK Organic Fertilizer in pieces for year and number of NPK Organic Fertilizer in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 5317.77 and a standard deviation equal to 900.752.  These parameters were estimated from the data.  Of the 12 nonexcluded points shown on the charts, 0 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 0 or more points beyond the limits just by chance is 1.0 if the data comes from the assumed distribution, we cannot reject the hypothesis that the process is in a state of statistical control at the 90% or higher confidence level.


X-BAR AND RANGE STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2012

X-bar Chart

-----------

UCL: +3.0 sigma = 6596.96 Centerline      = 5194.85

LCL: -3.0 sigma = 3792.74 0 beyond limits

Range Chart

-----------

UCL: +3.0 sigma = 2436.96 Centerline      = 745.563

LCL: -3.0 sigma = 0.0 0 beyond limits


X-BAR AND  R CHART DISCUSSION OF THE RESULTS FOR 2012

   This procedure creates X-Bar and R charts for number of NPK Organic Fertilizer in pieces for year and number of NPK Organic Fertilizer in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 5194.85 and a standard deviation equal to 660.96. These parameters were estimated from the data.  Of the 12 nonexcluded points shown on the charts, 0 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 0 or more points beyond the limits just by chance is 1.0 if the data comes from the assumed distribution, we cannot reject the hypothesis that the process is in a state of statistical control at the 90% or higher confidence level.

X-BAR AND S STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2012

X-bar Chart

-----------

UCL: +3.0 sigma = 6596.49 Centerline      = 5194.85

LCL: -3.0 sigma = 3793.21 0 beyond limits

S Chart

-------

UCL: +3.0 sigma = 1722.09 Centerline      = 527.193

LCL: -3.0 sigma = 0.0 0 beyond limits



X-BAR AND S CHART DISCUSSION OF THE RESULTS FOR 2012

   This procedure creates X-Bar and S charts for number of NPK Organic Fertilizer in pieces for year and number of NPK Organic Fertilizer in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 5194.85 and a standard deviation equal to 660.738.  These parameters were estimated from the data.  Of the 12 nonexcluded points shown on the charts, 0 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 0 or more points beyond the limits just by chance is 1.0 if the data comes from the assumed distribution, we cannot reject the hypothesis that the process is in a state of statistical control at the 90% or higher confidence level.


X-BAR AND RANGE STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2013

X-bar Chart

-----------

UCL: +3.0 sigma = 4895.23 Centerline      = 4232.0

LCL: -3.0 sigma = 3568.77 9 beyond limits




Range Chart

-----------

UCL: +3.0 sigma = 1152.73 Centerline      = 352.667

LCL: -3.0 sigma = 0.0 0 beyond limits


X-BAR AND R CHART DISCUSSION OF THE RESULTS FOR 2013

   This procedure creates X-Bar and R charts for number of NPK Organic Fertilizer in pieces for year and number of NPK Organic Fertilizer in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 4232.0 and a standard deviation equal to 312.648. These parameters were estimated from the data.  Of the 12 nonexcluded points shown on the charts, 9 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 9 or more points beyond the limits just by chance is 0.0 if the data comes from the assumed distribution, we can declare the process to be out of control at the 99% confidence level.


X-BAR AND S STUDY FOR NUMBER OF NPK ORGANIC FERTILIZER IN PIECES AND IN TONNES FOR YEAR 2013

X-bar Chart

-----------

UCL: +3.0 sigma = 4895.0 Centerline      = 4232.0

LCL: -3.0 sigma = 3569.0 9 beyond limits

S Chart

-------

UCL: +3.0 sigma = 814.585 Centerline      = 249.373

LCL: -3.0 sigma = 0.0 0 beyond limits


X-BAR AND S CHART DISCUSSION OF THE RESULTS FOR 2013

   This procedure creates X-Bar and S charts for number of NPK Organic Fertilizer in pieces for year and number of NPK Organic Fertilizer in tonnes for year.  It is designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean equal to 4232.0 and a standard deviation equal to 312.543. These parameters were estimated from the data.  Of the 12 nonexcluded points shown on the charts, 9 are beyond the control limits on the first chart while 0 are beyond the limits on the second chart.  Since the probability of seeing 9 or more points beyond the limits just by chance is 0.0 if the data comes from the assumed distribution, we can declare the process to be out of control at the 99% confidence level.


CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 SUMMARY

This research project aim at using quality control charts. The research is write up in five chapters starting with introduction of what a quality control is, its background, aims and objectives, scope, limitation and scope of the study where we end the chapter with some definitions of terms used in statistical quality control and specifically in this research. A review of literature on the previous researches were also discussed by citing the work of other researchers relevant to our study, also in chapter three a methodology of statistical quality control and data procedure were discussed, analysis of the collected data, results and discussion of the analyze data were discussed in chapter four and lastly in chapter five summary of the work, conclusion and recommendations were given.

5.2 CONCLUSION

This research work aim at determined the production process variation, by showing graphycally how one production level varies from the other and to reveal unwanted variation in quickly as would be detected with continous samplinh techniques of quality control. Base on the analysis and findings using X-Bar, R charts and S-chart for number of NPK Organic Fertilizer inspected in pieces and in tonnes for year 2009 to 2013 which is also designed to allow you to determine whether the data come from a process which is in a state of statistical control.  The control charts are constructed under the assumption that the data come from a normal distribution with a mean and standard deviation.

Therefore we conclude that the production of npk organic fertilizer is within the specification limit except some points which happens to be out of control due to some assignable causes.

5.3 RECOMMENDATIONS

From thus research work, it is obvious that for the Nigerian industries to improve and meet up with international specification/standard. The following are some recommendations;

The companies already involved in statistical quality control techniques should not be satisfied with the level of control and quality they possess but should also continue to strive for better results.

It is crucially important to any Nigerian company desiring to be of substance, locally investigational. To incorporate statistical quality control techniques into its manufacturing process.

The standard organization of Nigeria should work out on effectiveness measure to see that the standard of our goods is high in all our manufacturing industries. 

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