This book reviews current design paths for soft sensors, and guides readers in evaluating different choices. The book presents case studies resulting from collaborations between the authors and industrial partners. The solutions presented, some of which are implemented on-line in industrial plants, are designed to cope with a wide range of applications from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation.

This book reviews current design paths for soft sensors, and guides readers in evaluating different choices. The book presents case studies resulting from collaborations between the authors and industrial partners. The solutions presented, some of which are implemented on-line in industrial plants, are designed to cope with a wide range of applications from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation.

Advanced Control and Supervision of Mineral Processing Plants describes the use of dynamic models of mineral processing equipment in the design of control, data reconciliation and soft-sensing schemes; through examples, it illustrates tools integrating simulation and control system design for comminuting circuits and flotation columns. Coverage is given to the design of soft sensors based on either single-point measurements or more complex measurements like images. Issues concerning data reconciliation and its employment in the creation of instrument architecture and fault diagnosis are surveyed. In consideration of the widespread use of distributed control and information management systems in mineral processing, the book describes the platforms and toolkits available for implementing such systems. Applications of the techniques described in real plants are used to highlight their benefits; information for all of the examples, together with supporting MATLAB® code can be found at www.springer.com/978-1-84996-105-9.

Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.

Closes the gap between bioscience and mathematics-based process engineering This book presents the most commonly employed approaches in the control of bioprocesses. It discusses the role that control theory plays in understanding the mechanisms of cellular and metabolic processes, and presents key results in various fields such as dynamic modeling, dynamic properties of bioprocess models, software sensors designed for the online estimation of parameters and state variables, and control and supervision of bioprocesses Control in Bioengineering and Bioprocessing: Modeling, Estimation and the Use of Sensors is divided into three sections. Part I, Mathematical preliminaries and overview of the control and monitoring of bioprocess, provides a general overview of the control and monitoring of bioprocesses, and introduces the mathematical framework necessary for the analysis and characterization of bioprocess dynamics. Part II, Observability and control concepts, presents the observability concepts which form the basis of design online estimation algorithms (software sensor) for bioprocesses, and reviews controllability of these concepts, including automatic feedback control systems. Part III, Software sensors and observer-based control schemes for bioprocesses, features six application cases including dynamic behavior of 3-dimensional continuous bioreactors; observability analysis applied to 2D and 3D bioreactors with inhibitory and non-inhibitory models; and regulation of a continuously stirred bioreactor via modeling error compensation. Applicable across all areas of bioprocess engineering, including food and beverages, biofuels and renewable energy, pharmaceuticals and nutraceuticals, fermentation systems, product separation technologies, wastewater and solid-waste treatment technology, and bioremediation Provides a clear explanation of the mass-balance–based mathematical modelling of bioprocesses and the main tools for its dynamic analysis Offers industry-based applications on: myco-diesel for implementing "quality" of observability; developing a virtual sensor based on the Just-In-Time Model to monitor biological control systems; and virtual sensor design for state estimation in a photocatalytic bioreactor for hydrogen production Control in Bioengineering and Bioprocessing is intended as a foundational text for graduate level students in bioengineering, as well as a reference text for researchers, engineers, and other practitioners interested in the field of estimation and control of bioprocesses.

Over the last few years, interest in the industrial applications of AI and learning systems has surged. This book covers the recent developments and provides a broad perspective of the key challenges that characterize the field of Industry 4.0 with a focus on applications of AI. The target audience for this book includes engineers involved in automation system design, operational planning, and decision support. Computer science practitioners and industrial automation platform developers will also benefit from the timely and accurate information provided in this work. The book is organized into two main sections comprising 12 chapters overall: •Digital Platforms and Learning Systems •Industrial Applications of AI

PID Control for Industrial Processes presents a clear, multidimensional representation of proportional - integral - derivative (PID) control for both students and specialists working in the area of PID control. It mainly focuses on the theory and application of PID control in industrial processes. It incorporates recent developments in PID control technology in industrial practice. Emphasis has been given to finding the best possible approach to develop a simple and optimal solution for industrial users. This book includes several chapters that cover a broad range of topics and priority has been given to subjects that cover real-world examples and case studies. The book is focused on approaches for controller tuning, i.e., method bases on open-loop plant tests and closed-loop experiments.