China
Africa
Explore chapters and articles related to this topic
Force-System Resultants and Equilibrium
Published in Richard C. Dorf, The Engineering Handbook, 2018
Process control addresses the application of automatic control theory to the process industries. These industries typically involve the continuous processing of fluids and slurries in chemical reactors, physical separation units, combustion processes, and heat exchangers in industries such as chemicals, petroleum, pulp and paper, food, steel, and electrical power generation. Generally, the goal of process control is to reduce the variability of key process variables that influence safety, equipment protection, product quality, and production rate. To achieve reduced variability in these key variables, we must adjust selected manipulated variables. As a result, the “total variability” is not reduced, but it is transferred from important to less important areas of the process, such as cooling water, steam systems, and fuel distribution systems.
Process Monitoring and Control of Machining Operations
Published in Osita D. I. Nwokah, Yildirim Hurmuzlu, The Mechanical Systems Design Handbook, 2017
Robert G. Landers, A. Galip Ulsoy, Richard J. Furness
Process control is the manipulation of process variables (e.g., feed, speed, depth-of-cut) to regulate the processes. Machine tool operators perform on-line and off-line process control by adjusting feeds and speeds to suppress chatter, initiate an emergency stop in response to a tool breakage event, rewrite a part program to increase the depth-of-cut to minimize burr formation, etc. Off-line process control is performed at the process planning stage; typically by selecting process variables from a machining handbook or the operator’s experience. Computer-aided process planning4 is a more sophisticated technique which, in some cases, utilizes process models off-line to select process variables. The drawbacks of off-line planning are dependence on model accuracy and the inability to reject disturbances. Adaptive control techniques,5 which include adaptive control with optimization, adaptive control with constraints, and geometric adaptive control, view processes as constraints and set process variables to meet productivity or quality requirements. A significant amount of research in AI techniques such as fuzzy logic, neural networks, knowledge base, etc. which require very little process information has also been conducted.6
Computerized Control Systems: Basics
Published in Gauri S. Mittal, Computerized Control Systems in the Food Industry, 2018
The goals to be achieved are typically specified in terms of process variables related to the state of the process or to product quality attributes. Process control systems included in the design adjust the process to keep these process variables at the desired levels, or setpoints, by changing one or more of the process inputs. These inputs, or manipulated variables, may be varied manually, where an operator adjusts the process, or automatically, where some form of computer does the adjustment. While an operator is capable of very complex reasoning, automatic controllers can react much faster to process upsets and can give more consistent results. The choice must be based on the requirements of the process.
Developing a quality function deployment model for the Ethiopian leather industry: Requirements and solutions under linguistic variables
Published in Journal of Industrial and Production Engineering, 2023
Sisay Addis Filketu, Yeneneh Tamirat Negash
Process control is a technical factor of QIR, and it takes a preventive approach to quality improvement by designing processes that reduce variations. Well-controlled process implementation will ensure a standardized process and cut costs during production [21,31]. Process control focuses on each process to positively affect quality improvement, lower the number of defects, and improve customer value. Effective process control is required to incorporate the measurement of quality performance and the periodic review of internal processes that enable remedial actions to replace defective methods [32]. Afrin and Islam 12 underlined that for process control, using statistical process control tools and sharing process capability information with customers should be considered to reduce the chance of operation errors. Through successful process control, employees can provide a stable and sustained high-quality product to customers.
Optimal error governor for PID controllers
Published in International Journal of Systems Science, 2021
Luca Cavanini, Francesco Ferracuti, Andrea Monteriù
Proportional–Integral–Derivative (PID) controllers are used in a wide range of fields, e.g. process control and power converters, micro-manipulation and aerospace. PID algorithms are present in different forms in more than of the overall control loops developed (Åström & Hägglund, 2001), as standard single-loop controllers or as part of hierarchical, programmable or distributed control systems (Cavanini, Cimini, et al., 2017; Cavanini, Colombo, et al., 2017; Shi & Yang, 2018; Song et al., 2017). Despite the advanced control technology development of the last 20 years, PID still remains the most popular approach, due to the simple structure, conceptually easy to understand and provide adequate performance in the vast majority of applications (Liu & Daley, 2001). In fact, the three terms defining the PID control law fulfil the three most common required control performance: the proportional term provides a fast response to the actual error value without guaranteeing a good steady-state accuracy; the integral term, providing a slower response, yields the steady-state zero error and rejection of constant disturbances; the derivative term addresses fast error dynamics and is usually used in conjunction with filters to limit sensor noise effects (in this case, the controller is usually indicated as PIDF) (Knospe, 2006).
Reinforcement learning for process control with application in semiconductor manufacturing
Published in IISE Transactions, 2023
Yanrong Li, Juan Du, Wei Jiang
Process control is necessary for reducing variations in manufacturing processes to improve the quality and productivity of final products. For example, in semiconductor manufacturing, different unavoidable disturbances (e.g., tool-induced and product-induced disturbances) caused by various factors can influence the stability of manufacturing processes and the quality of final products (Su et al.,2007). Therefore, designing an efficient control strategy that reduces variations due to various disturbances is an important research problem in semiconductor manufacturing.