**2. Background and literature review**

There are four industrial revolutions so far [2] including first industrial revolution associated with discovery of water and steam power engine in 1784, the second industrial revolution in 1870 which involved *mass production* using electrical energy, the third industrial revolution in 1970 which introduced the use of information technology systems for automation and the fourth industrial revolution describing the present day which embraces Internet of Things (IOT) and cyber physical systems (CPS). The industrial revolutions which commenced over three centuries ago therefore have evolved and formed the backbone of manufacturing with each revolution providing a boost to productivity in the sector. Since the second industrial revolution mass production has sustained high volume production to satisfy global demand and needs. The manufacture of cars, guns and fast food are examples of mass production. It is the machine tool industry that gave rise to the idea of mass production because it motivated innovators in Britain and the United States to commence production of interchangeable parts.

Henry Ford was known to have invented an improved version of the assembly line for mass production when he mass produced the Curved Dash Oldsmobile. Ransom Eli Olds in 1901 in the USA. On this occasion he was said to have conceived the basic concept of the assembly line out of which 425 Curved Dash Oldsmobiles

#### *Mass Production Processes*

were manufactured. This significant development led the automotive manufacturing market over a period of 3 years from 1901 to 1904 [2]. It was also noteworthy that Henry Ford improved his design and installed the first conveyor belt assembly line around 1913 which reduced the building time of the Model T version to just 93 minutes. During that period, Ford became the world's-largest car manufacturer, having produced more than 15 million Model Ts by 1927 using the enhanced assembly line concept.

Other sectors have benefitted immensely from mass production such as the fast food and electronics industries to name a few. In the food industry, for instance, beverages, canned and bottled soft and alcoholic drinks and dried food packs are mass produced daily to satisfy huge global demand.

The advantages of mass production are [2]:


#### **2.1 Quality management during mass production**

During mass production, mass customisation and personalised production the emphasis and key objective is to produce very large quantities of the product within a limited time scale to satisfy demand. Monitoring the entire manufacturing process in terms of quality therefore could prove difficult considering that satisfactory manufacturing quality can only be achieved by rapid screening of materials at the input stage, transformation process stage within the manufacturing hub and finally at the output stage before shipment to the customer.

Recalls from mass production such as for cars, processed food and aircrafts have frequently been reported, even for the case where the rate of defects is only of the order of ppm or less. Even if the defect rate is of the order of ppm or less, most of the remaining safe products would also need to be recalled and may be destroyed or replaced by new ones. Such recalls are always cost intensive. During mass production it is very difficult using conventional quality control methods to find defects of the order of ppm or less at the stage of design and production.

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**Figure 3.**

*Model for quality control during mass production.*

*Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry*

Murakami [3] researched on a solution that could obviate the recall problem of the order of ppm during mass production. His new quality control method detects defects of the order of ppm or less for mass production products. This was based on the statistics of extremes successfully applied to the fatigue strength evaluation of defective materials. He confirmed that the same method could be applied, not only to mass production, but also for large machine components produced in large

**Figure 3** shows hypothetical model of an input, manufacturing transformation and output for a production process. Input to the process could be in form of raw materials, catalysts and information; the transformation process involves a transition stage where the input materials are combining under the relevant conditions, such as temperature, pressure, etc. to produce the target output. The model shows that for each of the three stages there would need to be control in place to achieve the target/desired quality. In a mass production/continuous processing environment it may not be feasible to evaluate every single item going through the process and also at the output. Rather, acceptance sampling is used for quality assurance. Siddiqui et al. [4] have reviewed the applications of analytical techniques including acceptance sampling in pharmaceutical analysis of drugs. They confirmed that pharmaceuticals may develop impurities at various stages of their development, transportation and storage, Consequently they need to be characterised and

**Table 1** shows common analytical techniques for quality control during mass production of chemicals. During chemical manufacture process analytical techniques are used to measure bulk materials, intermediates, impurities and degradation products. The aim is to characterise the quality of materials by setting limits of

The common analytical methods presented in **Table 1** include titrimetry, chromatography, spectroscopy, electrophoresis, electrochemical methods and

Acceptance sampling as a statistical method is also used to inspect bulk materials and mass-produced products to determine if they meet the required specifications. Acceptance sampling plan guarantees that the average quality level or percent defectives actually produced for consumers will not exceed a specified limit. It is therefore an effective way to ensure the high quality of mass-produced products and is usually conducted on the basis of a reference standard, or system

*DOI: http://dx.doi.org/10.5772/intechopen.90203*

numbers.

components quantified.

electrophoresis.

of inspection rules [4].

their active ingredient content [4].

#### *Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry DOI: http://dx.doi.org/10.5772/intechopen.90203*

Murakami [3] researched on a solution that could obviate the recall problem of the order of ppm during mass production. His new quality control method detects defects of the order of ppm or less for mass production products. This was based on the statistics of extremes successfully applied to the fatigue strength evaluation of defective materials. He confirmed that the same method could be applied, not only to mass production, but also for large machine components produced in large numbers.

**Figure 3** shows hypothetical model of an input, manufacturing transformation and output for a production process. Input to the process could be in form of raw materials, catalysts and information; the transformation process involves a transition stage where the input materials are combining under the relevant conditions, such as temperature, pressure, etc. to produce the target output. The model shows that for each of the three stages there would need to be control in place to achieve the target/desired quality. In a mass production/continuous processing environment it may not be feasible to evaluate every single item going through the process and also at the output. Rather, acceptance sampling is used for quality assurance.

Siddiqui et al. [4] have reviewed the applications of analytical techniques including acceptance sampling in pharmaceutical analysis of drugs. They confirmed that pharmaceuticals may develop impurities at various stages of their development, transportation and storage, Consequently they need to be characterised and components quantified.

**Table 1** shows common analytical techniques for quality control during mass production of chemicals. During chemical manufacture process analytical techniques are used to measure bulk materials, intermediates, impurities and degradation products. The aim is to characterise the quality of materials by setting limits of their active ingredient content [4].

The common analytical methods presented in **Table 1** include titrimetry, chromatography, spectroscopy, electrophoresis, electrochemical methods and electrophoresis.

Acceptance sampling as a statistical method is also used to inspect bulk materials and mass-produced products to determine if they meet the required specifications. Acceptance sampling plan guarantees that the average quality level or percent defectives actually produced for consumers will not exceed a specified limit. It is therefore an effective way to ensure the high quality of mass-produced products and is usually conducted on the basis of a reference standard, or system of inspection rules [4].

**Figure 3.** *Model for quality control during mass production.*

*Mass Production Processes*

bly line concept.

adapt.

repaired.

were manufactured. This significant development led the automotive manufacturing market over a period of 3 years from 1901 to 1904 [2]. It was also noteworthy that Henry Ford improved his design and installed the first conveyor belt assembly line around 1913 which reduced the building time of the Model T version to just 93 minutes. During that period, Ford became the world's-largest car manufacturer, having produced more than 15 million Model Ts by 1927 using the enhanced assem-

Other sectors have benefitted immensely from mass production such as the fast food and electronics industries to name a few. In the food industry, for instance, beverages, canned and bottled soft and alcoholic drinks and dried food packs are

d.Although capital and energy investment are increased total expenditure per

b.The workers are not highly motivated, since their work is very repetitive.

c.Manufacturing system is not very flexible, and production line is difficult to

d.If one part of the line breaks, the whole production process will halt until it is

During mass production, mass customisation and personalised production the emphasis and key objective is to produce very large quantities of the product within a limited time scale to satisfy demand. Monitoring the entire manufacturing process in terms of quality therefore could prove difficult considering that satisfactory manufacturing quality can only be achieved by rapid screening of materials at the input stage, transformation process stage within the manufacturing hub and finally

Recalls from mass production such as for cars, processed food and aircrafts have frequently been reported, even for the case where the rate of defects is only of the order of ppm or less. Even if the defect rate is of the order of ppm or less, most of the remaining safe products would also need to be recalled and may be destroyed or replaced by new ones. Such recalls are always cost intensive. During mass production it is very difficult using conventional quality control methods to find defects of the order of ppm or less at the stage of design and production.

mass produced daily to satisfy huge global demand. The advantages of mass production are [2]:

b.Labour costs are reduced substantially.

c.It engenders a faster rate of production.

The disadvantages of mass production are:

**2.1 Quality management during mass production**

at the output stage before shipment to the customer.

a.Mass production infrastructure is very expensive to set up.

unit of product is decreased.

e.Rate of production is enhanced.

a.It is usually 'automated' to the highest extent possible.

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#### **Table 1.**

*Common analytical techniques used for quality control during batch and mass production.*

Quality analysis by acceptance sampling is measured by the following variables [5]:


Advantages of acceptance sampling:


Limitations of acceptance sampling:

i.Sampling does not guarantee 100% batch quality and there is risk of substandard output.

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of the system's performance.

*Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry*

ii.Success of the system is dependent on, sampling randomness, quality characteristics to be tested, batch size and criteria of acceptance of lot.

• Producer's and consumer's risk: the acceptance or rejection of the whole batch of products in acceptance sampling depends upon the results of the sample inspected. There is always a chance that a sample may not be true representative of the batches or lots from which it is drawn. Consequently, there are two

• Producer risk (α): probability of a batch being good overall or even better than acceptable quality level (AQL) but due to bad sampling it is rejected. So this probability of rejection of a good lot which otherwise would have been

• Consumer risk (β): probability of a bad batch or substandard quality being accepted however produces a good sample and consequently accepted. So this probability of a defective lot being accepted which otherwise would have been

Guoa et al. [6] designed a quality control mechanism by scoring the quality of input, the transformation process and the output score. In their study they developed a six component process analysis turtle diagram quality scoring system. The assessment system is based on input material resources, the process support, quality control standard, quality manufacturing control method and the output, forming a quality control mechanism analysis model presented as a

**Figure 4** adapted from [6] shows the radar model for quality control mechanism

during manufacture. It shows the six variables that would need to be monitored during a production process as input, output, quality monitoring, quality control standards, process support and physical resources. It also shows the levels of expec-

Switching from batch to continuous pharmaceutical mass production offers several advantages, such as increased productivity, steady product quality and decreased costs. Kirchengast et al. [7] presented a control strategy for direct compaction on a continuous tablet production line consisting of two feeders, one blender and a tablet press (TP). They also applied a data-driven, linear modelling approach to develop a Smith predictor for active pharmaceutical ingredient concentration control and a model predictive controller responsible for the TP hopper level. In case of severe concentration variations the system could discard out-of-specification material before it entered the TP. The authors tested effectiveness of the control concept in a simulation as well as by implementing it on a real

Mass manufacture of micro products in the present day is quite challenging since tools, materials and technologies have to be scaled down from the macro to the micro domain. This is mainly because downscaling of the basic classical process would lead to unexpected process behaviour which poses new challenges for inprocess quality inspection desiring a reliable quality control. To implement this in a mass production environment therefore, new strategies to plan the process while focussing on the logistics of the quality parameters would become essential. Weimer [8] introduced a closed-loop quality control strategy for bulk production in micro cold forming. A discrete event simulation model incorporating characteristics of optical quality inspection and general process parameters allows the quantification

*DOI: http://dx.doi.org/10.5772/intechopen.90203*

accepted is known as producer's risk (α).

rejected is known as consumer risk (β).

tation for the variables measured quantitatively.

types of risk;

radar map.

pilot plant.


Guoa et al. [6] designed a quality control mechanism by scoring the quality of input, the transformation process and the output score. In their study they developed a six component process analysis turtle diagram quality scoring system. The assessment system is based on input material resources, the process support, quality control standard, quality manufacturing control method and the output, forming a quality control mechanism analysis model presented as a radar map.

**Figure 4** adapted from [6] shows the radar model for quality control mechanism during manufacture. It shows the six variables that would need to be monitored during a production process as input, output, quality monitoring, quality control standards, process support and physical resources. It also shows the levels of expectation for the variables measured quantitatively.

Switching from batch to continuous pharmaceutical mass production offers several advantages, such as increased productivity, steady product quality and decreased costs. Kirchengast et al. [7] presented a control strategy for direct compaction on a continuous tablet production line consisting of two feeders, one blender and a tablet press (TP). They also applied a data-driven, linear modelling approach to develop a Smith predictor for active pharmaceutical ingredient concentration control and a model predictive controller responsible for the TP hopper level. In case of severe concentration variations the system could discard out-of-specification material before it entered the TP. The authors tested effectiveness of the control concept in a simulation as well as by implementing it on a real pilot plant.

Mass manufacture of micro products in the present day is quite challenging since tools, materials and technologies have to be scaled down from the macro to the micro domain. This is mainly because downscaling of the basic classical process would lead to unexpected process behaviour which poses new challenges for inprocess quality inspection desiring a reliable quality control. To implement this in a mass production environment therefore, new strategies to plan the process while focussing on the logistics of the quality parameters would become essential. Weimer [8] introduced a closed-loop quality control strategy for bulk production in micro cold forming. A discrete event simulation model incorporating characteristics of optical quality inspection and general process parameters allows the quantification of the system's performance.

*Mass Production Processes*

variables [5]:

**Table 1.**

analysis (5) Electrophoresis (6) Titrimetry

Quality analysis by acceptance sampling is measured by the following

*Common analytical techniques used for quality control during batch and mass production.*

**Process analytical technique (PAT) Variants of the technique**

(3) Electrochemical method a. Voltammetry

(4) Flow injection and sequential injection

(1) Chromatography a. High performance thin layer chromatography

(2) Spectroscopy a. Nuclear magnetic resonance spectroscopy

b. Thin layer chromatography

d. Gas chromatography

b. Infra-red spectroscopy c. Near infra-red spectroscopy d. Spectrophotometry e. Flourimetry f. Phosphorimetry

b. Polarography c. Amperometry d. Potentiometry

c. High performance liquid chromatography

a.Acceptable quality level (AQL): this is the desired quality level at which the probability of acceptance is high. It is described as the maximum proportion of defectives which the consumer finds acceptable. it is therefore the maximum percent defectives that can be considered satisfactory during sampling.

b.Lot tolerance percent defective (LTPD) or reject able quality level (RQL): this is the quality level at which the probability of acceptance is low and below this level the batch is rejected. This could be described as the dividing line between good and bad lots. Lots at this quality level however, are considered to be poor

i.It is applicable to industries where there is mass production which follows a

iii.Products that are delicate to handling during inspection can be inspected by

i.Sampling does not guarantee 100% batch quality and there is risk of sub-

and are classed as average outgoing quality (A.O.Q )

ii.The method is economical and easy to understand.

iv.Acceptance sampling enhances scheduling and delivery times.

Advantages of acceptance sampling:

set production procedure.

Limitations of acceptance sampling:

sampling.

standard output.

**138**

#### **Figure 4.**

*Model for quality control mechanism.*

## *2.1.1 Quality control strategies for mass production*

A three-level quality control strategy for a continuous manufacturing process was proposed to maintain the quality of the product in response to potential variations or disturbance in the process, equipment conditions, incoming raw materials, or environmental factors over time Yu et al. [9]. A Level-3 quality control strategy imposes strict constraints on attributes and parameters that could affect product quality while also relying on rigorous end-product testing that should guarantee final product quality. Tight quality control of this nature is also used in a batch manufacturing environment and quality by testing (QbT) situation. This is managed by tracking a manufacturing operation to ensure that relevant parameters are maintained within the desired constraint. It is also necessary at the early stage of drug development to have a clear understanding of how raw material and process variability could affect product quality to fully appreciate the level of quality control. There is a perceived view that this approach is not feasible to be effectively implemented in continuous manufacturing processes.

The US Food and Drug Administration (FDA) recognised that increased testing does not necessarily improve product quality and thus quality must be built into the product [10], following the QbD concept. Consequently, pharmaceutical quality by

**141**

*Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry*

design (QbD) has now evolved guided by standardised documents associated with Pharmaceutical Development, Quality Risk Management, Pharmaceutical Quality

Quality by design (QBD) finally matured and evolved into quality by control (QbC) which describes the design and operation of a robust manufacturing system. This is normally achieved by an integrated and comprehensive process control underpinned by automation principles and also based on a clear understanding of the process. Overall therefore, Quality by Control enables a continuous monitoring and control of process operations and systematic release of the correct specification

**Figure 5** shows a plot of drug quality in development stages vs. Product and Process knowledge. It demonstrates the evolutionary process of batch manufacturing through to continuous manufacturing and then smart manufacturing and the corresponding quality assurance checks in place. As drug quality development progresses from the batch to continuous and then smart manufacturing, quality assurance changes from quality by testing to quality by design and then consolidat-

In continuous chemical production lines, materials are transformed into final products and by-products and sometimes the activities take place in different departments. Overall, the quality of the final products is dependent on the quality of the input materials, the state of the relevant manufacturing equipment and the performance of the operators. However, out of control situations along production lines would normally lead to loss in operation time, material and financial resources. Sahebjamnia et al. [11] proposed a Fuzzy Q-learning Multi-Agent Quality Control System (FQL-MAQCS) to control a continuous chemical production line. The system manages unforeseen circumstances during production through a multiagent based system. It consists of quality control executor, process data analyst, central decision-maker, departmental decision-maker and knowledge/rule manager agents. The system is also self-learning, updated periodically and information gathered could be stored in a knowledge base. FQL-MAQCS has been tested in a real case study situation and the results demonstrate the usefulness, robustness and

System and Development and Manufacture of Drug Substances [10].

*The systematic progression in quality assurance via QbT, QbD and QbC [10].*

ing to quality by control based on standards and requirements.

*2.1.2 Quality control during bulk pharmaceutical production*

capability of the developed quality control system.

*DOI: http://dx.doi.org/10.5772/intechopen.90203*

of manufactured products in real time.

**Figure 5.**

*Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry DOI: http://dx.doi.org/10.5772/intechopen.90203*

#### **Figure 5.**

*Mass Production Processes*

*2.1.1 Quality control strategies for mass production*

A three-level quality control strategy for a continuous manufacturing process was proposed to maintain the quality of the product in response to potential variations or disturbance in the process, equipment conditions, incoming raw materials, or environmental factors over time Yu et al. [9]. A Level-3 quality control strategy imposes strict constraints on attributes and parameters that could affect product quality while also relying on rigorous end-product testing that should guarantee final product quality. Tight quality control of this nature is also used in a batch manufacturing environment and quality by testing (QbT) situation. This is managed by tracking a manufacturing operation to ensure that relevant parameters are maintained within the desired constraint. It is also necessary at the early stage of drug development to have a clear understanding of how raw material and process variability could affect product quality to fully appreciate the level of quality control. There is a perceived view that this approach is not feasible to be effectively implemented in continuous manufac-

The US Food and Drug Administration (FDA) recognised that increased testing does not necessarily improve product quality and thus quality must be built into the product [10], following the QbD concept. Consequently, pharmaceutical quality by

**140**

**Figure 4.**

*Model for quality control mechanism.*

turing processes.

*The systematic progression in quality assurance via QbT, QbD and QbC [10].*

design (QbD) has now evolved guided by standardised documents associated with Pharmaceutical Development, Quality Risk Management, Pharmaceutical Quality System and Development and Manufacture of Drug Substances [10].

Quality by design (QBD) finally matured and evolved into quality by control (QbC) which describes the design and operation of a robust manufacturing system. This is normally achieved by an integrated and comprehensive process control underpinned by automation principles and also based on a clear understanding of the process. Overall therefore, Quality by Control enables a continuous monitoring and control of process operations and systematic release of the correct specification of manufactured products in real time.

**Figure 5** shows a plot of drug quality in development stages vs. Product and Process knowledge. It demonstrates the evolutionary process of batch manufacturing through to continuous manufacturing and then smart manufacturing and the corresponding quality assurance checks in place. As drug quality development progresses from the batch to continuous and then smart manufacturing, quality assurance changes from quality by testing to quality by design and then consolidating to quality by control based on standards and requirements.

#### *2.1.2 Quality control during bulk pharmaceutical production*

In continuous chemical production lines, materials are transformed into final products and by-products and sometimes the activities take place in different departments. Overall, the quality of the final products is dependent on the quality of the input materials, the state of the relevant manufacturing equipment and the performance of the operators. However, out of control situations along production lines would normally lead to loss in operation time, material and financial resources. Sahebjamnia et al. [11] proposed a Fuzzy Q-learning Multi-Agent Quality Control System (FQL-MAQCS) to control a continuous chemical production line. The system manages unforeseen circumstances during production through a multiagent based system. It consists of quality control executor, process data analyst, central decision-maker, departmental decision-maker and knowledge/rule manager agents. The system is also self-learning, updated periodically and information gathered could be stored in a knowledge base. FQL-MAQCS has been tested in a real case study situation and the results demonstrate the usefulness, robustness and capability of the developed quality control system.

Pharmaceutical production is now progressing more into mass personalization, down to lot size *N* = 1. Each product is not only produced as part of a batch during mass production but it is also designed uniquely for the benefit of a specific patient. Based on demand certain drugs are custom designed and manufactured for large hospital or community pharmacies. An ongoing trend is an increased usage of contract manufacturing/filling by most pharmaceutical companies. Highly flexible aseptic production and filling lines have now become trendy until recently was not feasible through filling line concepts available in the present day but only through highly flexible automated systems driven by robotics [12]. The popular technology of individualised/customised mass production of cars in terms of colour, engine specification and performance, wheels, lighting and extras is still remote in current pharmaceutical production technologies. If successfully implemented, this would be a revolution in pharmaceutical manufacturing and also for the Food and Drug Administration (FDA). Bhaskar [13] designed an advanced model predictive control (MPC) architecture integrated with a novel real-time tablet weight measurement method aimed to develop and implement continuous direct compacting tablet manufacturing pilot-plant which has the potential to control tablet weight and tablet breaking force simultaneously by systematically decoupling and cascading the control loops. The predictive control algorithm (PCA) was superior to the proportional, integral and derivative (PID) controller and consequently could be utilised for a wide range of applications to improve the quality of pharmaceutical products during continuous manufacturing. The MPC enabled control of the main compression force and pre compression force using main compression height and fill depth respectively as the actuators. The researchers claimed that developing this technology made it possible to measure tablet weight and other variables that enhanced manufacturability of pharmaceutical tablets.

Lakerveld et al. [14] in a separate study designed a plant-wide control structure for a continuous pharmaceutical pilot plant used to classify control objectives. By means of simulation software they demonstrated that for selected parameters the critical quality attributes (CQAs) of the final product can be kept close to specification in the presence of significant and persistent disturbances. This shows flexibility to control CQAs independently of each other.

Mesbah et al. [15] developed a control strategy based on a non-linear moment model whose optimal operation was based on manipulation of heat input to a crystallizer such that a maximal allowable crystal growth rate was maintained during a production process. The feedback structure of the control framework enabled the optimizer to reject process uncertainties and accurately account for plant-model mismatch while fulfilling product quality requirements.

Advanced feedback control concepts which are capable of improving performance of batch processes, as well as enable technologies from batch to continuous manufacturing were developed by Nagy et al. [16]. These were used in the improvement of pharmaceutical particulates, especially in the application to continuous drug substance/product manufacturing. Mathematical modelling of the optimal design, start-up and control of anti-fouling and continuous crystallisation processes to achieve and maintain the desired controlled stage of operation was demonstrated. Su et al. [17] demonstrated the use of a rotary tablet press, integrated into a pilot scale continuous direct compaction process. The outcome was that active process control which was based on product and process knowledge and advanced model-based techniques, data reconciliation, model predictive control and risk analysis, appeared to be indispensable when implementing a comprehensive Quality by Control. It also appeared to guarantee robustness and production efficiency. Recently, Singh [18] developed an automated version of the continuous pharmaceutical manufacturing pilot plant. The feeder, comill, blender and tablet

**143**

per million (ppm).

*Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry*

press unit operations of the pilot plant were integrated with a centralised control platform enabling the whole plant to be operated automatically. Data generated from all the unit operations were capable of being collected and stored systemati-

Contamination quality control is a huge challenge during manufacturing especially for products with ultra-low tolerance for impurities and contaminants. For instance during the manufacture of premium chemicals, highly sterile pharmaceuticals and hygienic products and other products with high purity requirements and specifications, it is essential to provide an environment that will obviate any form of contamination. A common source of contamination is from manufacturing machines and equipment that can conceal microscopic particles and microbes. Decontamination can be achieved through effective cleaning and disinfecting manufacturing equipment at regular intervals and ensuring that all surfaces exposed to raw materials and products can be reached by the cleaning and disinfection processes. Hydrogen peroxide is widely recommended for the cleaning and fumigation process and therefore all machine housing and operating components and interfaces need to be robustly designed to minimise any form of contamination from these. Manufacturing equipment therefore should be designed to be resistant to chemicals, display low adsorption affinity for hydrogen peroxide during fumigation and also its rapid desorption during aeration in order to speed up the production process [12]. Kraemer et al. [19] researched on clean ability test of a robot contamination with a water-based fluorescing test contamination. The researchers mixed riboflavin in ultra-pure water which was then allowed to dry onto the manufacturing surfaces. On inspection under UV light before ultra and after manual wipe cleaning with a pre wetted polyester knitting cleanroom wipe using ultrapure water, they observed areas that were difficult to clean because of the use of the fluorescing pigment

*DOI: http://dx.doi.org/10.5772/intechopen.90203*

*2.1.2.1 Contamination quality control during manufacturing*

riboflavin. This included depressions, indentations and edges.

**3. Case study: process flow diagram for the manufacturing process** 

Amoxicillin oral suspension is an antibiotic which most commonly utilised for first line treatment of middle ear infections and for pneumonia, skin infections and strep throat as well. Active compound used in its formulation has chemical formula C16H19N3O5S with molar mass of 365.4. It is also called phenoxymethyl penicillin. **Figure 6** shows a variety of Amoxicillin capsules and tablets while **Figure 7** shows the structural formula of Amoxicillin. **Figure 8** is GSK Manufacturing Plant at Irvine. **Figure 9** shows the process flow diagram for manufacture of Amoxicillin [20]. The process starts from stage A involving the reaction of the relevant raw materials [6-aminopenicillanic acid (6 APA), water and hydroxy phenyl glycine methyl ester (HPGME) and phenyl glycine acylase (PGA)] necessary for manufacture and finishes at stage O which is the drying process. Quality control of the manufacturing process takes place at every stage to ensure that the finished product in a dried form has the target consistency of 100% amoxicillin with minimum impurity. Most of the unit operations up till this stage are mainly batch manufacturing processes. The dried amoxicillin powder would need to be quality tested by acceptance sampling using relevant analytical techniques to establish conversion consistency and consequently any impurities beyond the recommended concentration in parts

cally in a data base.

**of amoxicillin**

press unit operations of the pilot plant were integrated with a centralised control platform enabling the whole plant to be operated automatically. Data generated from all the unit operations were capable of being collected and stored systematically in a data base.
