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Chemical process control. --- Chemical process control --- Automation. --- Process control

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Production engineering and advanced control systems make it possible to respond to the increasing needs of nations around the world. This book presents some of the new ideas, creative applications, and process optimization methods for more efficient utilization of resources available for improving productivity and reliability or lessening the times and costs of industrial production processes. It presents a variety of chapters on the development of production techniques and systems including abrasive waterjet machining, additive manufacturing, and production management. It also discusses the methods used for the development and improvement of control mechanisms with the objective of improving the reliability and efficiency of production systems. This book is useful for researchers, engineers, and experts working in different areas of production engineering and manufacturing control systems.

Production engineering. --- Process control.

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Statistical process control methodology was developed by Walter Shewhart in the 1920s as part of his work on quality control in industry. Shewhart observed that quality is about hitting target specifications with minimum variation. While every process is subject to variation, that variation can arise from 'common cause' variation, inherent in the process, or 'special cause' variation which operates from outside of that process. This distinction is crucial because the remedial actions are fundamentally different. Reducing common cause variation requires action to change the process; special cause variation can only be addressed if the external cause is identified. Statistical process control methodology seeks to distinguish between the two causes of variation to guide improvement efforts. Using case studies, this Element shows that statistical process control methodology is widely used in healthcare because it offers an intuitive, practical, and robust approach to supporting efforts to monitor and improve healthcare. This title is also available as Open Access on Cambridge Core.

Process control --- Statistical methods.

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Process control --- Contrôle industriel

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The goal of bioprocessing is to optimize process variables, such as product quantity and quality, in a reproducible, scalable, and transferable manner. However, bioprocesses are highly complex. A large number of process parameters and raw material attributes exist, which are highly interactive, and may vary from batch to batch. Those interactions need to be understood, and the source of variance must be identified and controlled. While purely data-driven correlations, such as chemometric models of spectroscopic data, may be employed for the understanding how process parameters are related to process variables, they can hardly be deployed outside of the calibration space. Currently, mechanistic models, models based on mechanistic links and first principles, are in the focus of development. They are perceived to allow transferability and scalability, because mechanistics can be extrapolated. Moreover, the models deliver a large range of hardly-measureable states and physiological parameters. The current Special Issue wants to display current solutions and case studies of development and deployment of hybrid models and multi-parametric control of bioprocesses. It includes: -Models for Bioprocess Monitoring -Model for Bioreactor Design and Scale Up -Hybrid model solutions, combinations of data driven and mechanistic models. -Model to unravel mechanistic physiological regulations -Implementation of hybrid models in the real-time context -Data science driven model for process validation and product life cycle management.

Biotechnological process control. --- Bioprocess control --- Biotechnical process control --- Biotechnology --- Process control