Describe with diagrams an example “process control system” of your choice.
The rate of adoption of industrial automation in the food industry has been recorded to take shape at relatively lower rates in comparison to the other industries, and manual handling and assembling of food tend to have wide coverage. This is attributed to the fact that food products have a diverse range of features among them fruits, vegetables and meat which thus call for more individualized handling unit basis. This, in turn, calls for high levels of flexibility in the industrial automation of the food industry in comparison to other mature industries. Factoring in the diversity in the food industry, it becomes a challenge to establish a generic automation solution as a result of the biological changes in the shape and size of raw materials in as much as some of the features such as labeling, quality control, palletizing and packaging are generic in the production process.
Process control diagram (Kostaropoulos, 2015)
Despite the numerous hiccups associated with automation in the food industry, it still becomes essential to automate the industry (Hitzmann, 2017, p. 176). The need to automate food industry has been necessitated by the goal to achieve competitive advantage requirements which are only achievable through improved quality of food and improved productivity. Through automation in food processing, efficiency in the flow of work and use of labor is achieved and thus a higher efficiency in the allocation of resources. Bearing the inefficiency, tedious and inconsistent nature of human visual inspection, a need to develop more complex, reliable and quality assuring technology was needed. Such technologies as computer vision have enhanced automatic inspections of food products thereby enhancing higher levels of accuracy and consistency in the evaluation of the food quality (Kostaropoulos, 2015, p. 596). Through food automation, it has been able to collect, store and track data of all the events and processes in food production operations which then allow the manufacturers to ascertain compliance with the environmental and food hygiene provisions and regulations.
Food processing industries have been moved to increased automation dues to the rise in the demand for higher quality products and the flexibility in sharing equipment used in the manufacturing process among other factors. In response to these rising demands, the control system vendors have managed to respond to the situations through the provision of the appropriate hardware and modular software capability to give the process engineer an opportunity to focus on the strategy of process control as opposed to concentrating on the control system design (Lamb, 2013, p. 313). Still, through the development of sensors that can be used in the measurement of the subjective and quality features among them smell and taste new vistas have provided in automation application in the food processing industry.
Reduction in labor costs is yet another factor for the heightened interest in automation of food production. A lot of manual work in food processing advocates for rapid and repetitive movement. As a result of the tiresome environment of work besides the low skilled nature of the work, low motivation levels are obtained that culminate into delicate safety issues and poor quality of food. Through automating repetitive tasks, these concerns can be improved (Rahman, 2012, p. 1588). Depending on the type and specific requirements of the manufacturers, automated systems in the processing and production of food vary in sizes and functions. While some food products will require hard automation, i.e., fixed processing sequence for example beverages, snacks, and dairy foods, others will call for a soft customized solutions to address specific needs due to the complexity in their nature.
Sensors tend to be the main interphase that exists between a control system and the process. Sensors serve the following functions in the processing industry:
Sensors have been designed to meet the specific needs of the food processing industry in a bid to achieve the growing demand for reliable online low-cost sensors in the food industry. It will be more important in the future to include in-line sensors in the automation of food industry to aid in the monitoring and control of quality (Caldwell, 2012, p. 444).
The size of the industry and the nature of the plants within the same industry are the determinants of the level of automation in the industry (Mutlu, 2016, p. 322). As a result of changes in the technology, numerous food processing plants have evolved from the small and more conservative approach in processing to more complex and technology-based processing. Automation in the food industry started with the use of programmable controllers and single-board computers in the manufacturing equipment as well as the use of very simple control systems. Due to their simplicity of maintenance and operations, these devices were easily adopted by the manufacturers. However, they were only limited to whatever services they could offer which were the replacements of relays, counters, and timers (Zhou, 2016, p. 198).
To meet and maintain competitive advantages, numerous changes were done to the manufacturing process that ensured flexibility in the production process. These changes allowed for flexibility and ease of configuration of the systems used in the control and process management systems. It is possible through technology for food manufacturers to either upgrade their programmable logic controllers (PLCs) or install distributed control systems (DCSs) (Sandeep, 2011, p. 188). It is possible to build upon the already existing programmable logic systems to come up with a fully integrated control system that can be used in the execution of various functions among them management of raw materials, process control and financial management and reporting.
The principle of operation of a distributed control system lies in breaking down into smaller subsystems a large application to bring down the level of control to the level of the unit when deemed appropriate to reduce the response time of the system. Through this, exchange of information between the various control units is made possible hence allowing for the integration of decision making a product line or plant level (Zongwei, 2015, p. 312). Different levels of logical and conceptual sophistication are involved in the control of any process, and thus numerous ways are used in the categorization of control functions into various levels of hierarchy.
Discussed below is a three-level hierarchy structure as shown in the diagram. In this hierarchy, the input/output devices, actuators, and sensors have direct interaction with the process. At this level, regulatory controls of the variables of the process among them pressure and temperature are carried out using proportional, integral and derivative control as well as logic operations including time and event-based control actions (Kostaropoulos, 2015, p. 710). Individual controllers are used in operation at the unit level control in regulating the equipment for example blenders.
Numerous options are available for an improvement of the control at this level. One of such is the use of smart transmitters containing some amount of data processing. This device is used for the monitoring of the variables of the process closely. It is also used in linearization, automatic calibration, as well as auto-tuning.
The second hierarchical level is the tactical level and is the level at which control improvement occurs through the incorporation of process parameters that are independent. An example would be the case where product quality is out of specification (Rahman, 2012, p. 1056). Under such a case, there may need to modify the recipe or the set-point profiles online. Modification of the set-point or the recipe calls for a model of the process which can be set of mathematical description, heuristic rules or both. A distributed control system allows for user programming facility for intelligent control of such processes. Preconfigured software modules are used by vendors for batch process modules that are linked and configured to the batch automation processes as opposed to the use of programming by users (Zhou, 2016, p. 255). This helps the process engineer to easily configure or modify the software without necessarily having to hire the services of a computer specialist.
The third level of the hierarchy hosts communication networks that are used in the exchange of information both for remote and local communication. Exchange of information is ideal for integration and coordination of different subsystems. Distributed controls systems can support gateways that corporate computing systems are hence enhancing real-time window management into the operations of a plant (Lamb, 2013, p. 254).
An illustration of the various levels of process control system (Silva, 2012, p. 244)
Distributed control systems are minute logical blocks that incorporate incremental programming and checkouts, alongside easy identification and maintenance of a fault. Distributed control systems allow the breakdown of the software systems into smaller logical pieces without necessarily disintegrating the hardware. This enhances efficiency and simplicity of the system design. The use of the software systems allows efficient management of recipe, analysis of the data on production as well as accurate recording. It also allows for functions touching on statistical and quality control (Rahman, 2012, p. 1598). Another benefit of distributed control systems is the removal of automation through an integrated system. Distributed control systems allow communication across the various subsystems involved in the manufacturing process hence allowing for easy coordination of the process of production as opposed to working with isolated islands which are based on single controllers.
Distributed control systems gracefully degrade form failure. Through the breakdown of the systems, there is the distribution of the system providing an autonomy that is large enough to prevent any chances of massive failure (Zhou, 2016, p. 128). This is opposed to the case of a direct digital control strategy that depends on a single computer to manage the whole process. In case of failure of the central control computer, the whole systems will fail to lead to stoppage of the construction process. Distributed control systems have an open architecture. This allows for easy integration of the existing devices into the system. An example is the programmable logic controls which are already available in the factory are very important and required that the chosen distributed control systems can communicate with the other devices from the system vendors (Mutlu, 2016, p. 288).
In conclusion, distributed control systems provide for flexibility and expansion that an industry undergoing rapid transformation needs as the case of the food industry currently. Distributed control systems have a significant impact on the optimization of the production process through their ease of implementation and operation. It offers opportunities for adoption of even more sophisticated tools usable in the scheduling and planning of production, control of quality among other functions.
References
Caldwell, D. G. (2012). Robotics and Automation in the Food Industry: Current and Future Technologies. London: Elsevier Science.
Hitzmann, B. (2017). Measurement, Modeling, and Automation in Advanced Food Processing. New York: Springer.
Kostaropoulos, G. S. (2015). Handbook of Food Processing Equipment. New York: Springer.
Lamb, F. (2013). Industrial Automation: Hands On. Cambridge: McGraw Hill Professional.
McFarlane, I. (2012). Automatic Control of Food Manufacturing Processes. New York: Springer Science & Business Media.
Mutlu, M. (2016). Biosensors in Food Processing, Safety, and Quality Control. Manchester: CRC Press.
Rahman, J. A. (2012). Handbook of Food Process Design. Sydney: John Wiley & Sons.
Sandeep, K. P. (2011). Thermal Processing of Foods: Control and Automation. London: John Wiley & Sons.
Zhou, S. P. (2016). RFID and Sensor Network Automation in the Food Industry: Ensuring Quality and Safety through Supply Chain Visibility. Kansas: John Wiley & Sons.
Zongwei, L. (2015). Robotics, Automation, and Control in Industrial and Service Settings. London: IGI Global.
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