A Framework for Learning System for Complex Industrial Processes (original) (raw)

Chapter A Framework for Learning System for Complex Industrial Processes

2021

Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic le...

Development of Learning Modules for Process Plant Operation

2020 ASEE Virtual Annual Conference Content Access Proceedings

joined the faculty as an Assistant professor of Chemical Engineering at West Virginia University (WVU) in January 2013. He is now Associate Professor of Chemical Engineering since August, 2019. His research group at WVU focuses on the development and implementation of process systems engineering methods for process design and intensification, advanced control and state estimation, modular energy systems and sustainability. He received his B.S. degree from the University of São Paulo in 2003 and his Ph.D. from Tufts University in 2007, both in Chemical Engineering. Upon completion of his Ph.D., he was a research associate at the University of Wisconsin-Madison and a postdoctoral associate at the University of Minnesota. Dr.

A Course on Process Design and Operation in an Engineering Technology Program

2011 ASEE Annual Conference & Exposition Proceedings

Master of Science degree in Chemical Engineering/Process Control from the University of Alberta, and the Doctor of Philosophy degree in Chemical Engineering/Process Control from Lehigh University. His research interests focus on process control systems, process modeling and simulation, artificial intelligence and expert systems. His professional experience includes management and technical positions with chemicals, refining, and consulting companies. He has published and presented a number of papers on advanced process control, real-time optimization systems, adaptive control, artificial intelligence and expert systems. He is a member of AIChE.

How to increase the performance of models for process optimization and control

Journal of Biotechnology, 1997

Some key aspects of obtaining hybrid process models which perform well and that can be used in process supervision, optimization and control are discussed from the point of view of the benefit/cost-ratio. The importance of starting with a clear definition of the problem and a corresponding quantitative objective function is shown. In order to enhance the benefit/cost-ratio above the threshold of acceptance, a series of procedures is proposed: in the beginning an exploratory process data analysis is suggested to classify the process variables according to their importance and to facilitate the development of black- and grey-box models. Efficient validation of the model is shown to be indispensable. Hybrid model approaches proved to have to significant advantages, since they allow the activation of a larger portion of the available a-priori knowledge. Applications of hybrid models with respect to process optimization require new techniques, since the classical approaches are too difficult to use and are restricted to well-performing models. Finally, powerful software tools are required to implement the different algorithms at the production plants and to allow the efficient conversion of the ideas to real benefits.

Artificial intelligence for monitoring and supervisory control of process systems

Complex processes involve many process variables, and operators faced with the tasks of monitoring, control, and diagnosis of these processes often find it difficult to effectively monitor the process data, analyse current states, detect and diagnose process anomalies, or take appropriate actions to control the processes. The complexity can be rendered more manageable provided important underlying trends or events can be identified based on the operational data (Rengaswamy and Venkatasubramanian, 1992. An Integrated Framework for Process Monitoring, Diagnosis, and Control Using Knowledge-based Systems and Neural Networks. IFAC, Delaware, USA, pp. 49–54.). To assist plant operators, decision support systems that incorporate artificial intelligence (AI) and non-AI technologies have been adopted for the tasks of monitoring, control, and diagnosis. The support systems can be implemented based on the data-driven, analytical, and knowledge-based approach (Chiang et al., 2001. Fault Detection and Diagnosis in Industrial Systems. Springer, London, Great Britain). This paper presents a literature survey on intelligent systems for monitoring, control, and diagnosis of process systems. The main objectives of the survey are first, to introduce the data-driven, analytical, and knowledge-based approaches for developing solutions in intelligent support systems, and secondly, to present research efforts of four research groups that have done extensive work in integrating the three solutions approaches in building intelligent systems for monitoring, control and diagnosis.

PLS: A versatile tool for industrial process improvement and optimization

Applied Stochastic Models in Business and Industry, 2008

Modern industrial processes are characterized by acquiring massive amounts of highly collinear data. In this context, partial least-squares (PLS) regression, if wisely used, can become a strategic tool for process improvement and optimization. In this paper we illustrate the versatility of this technique through several real case studies that basically differ in the structure of the X matrix (process variables) and Y matrix (response parameters). By using the PLS approach, the results show that it is possible to build predictive models (soft sensors) for monitoring the performance of a wastewater treatment plant, to help in the diagnosis of a complex batch polymerization process, to develop an automatic classifier based on image data, or to assist in the empirical model building of a continuous polymerization process.

Self-Learning approach to support lifecycle optimization of Manufacturing processes

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013

Modern manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solutions to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the resources and an improved quality of products. The shoe industry provides a fertile ground in this direction since traditionally the production and manufacturing of shoes involves a wide variety of materials and a large number of both operations and machines characterized by a huge number of parameters as well. Thereby, the optimization of manufacturing process parameters during production activities is recognized as one of the most important task. As a matter of fact, the selection of the best set of manufacturing process parameters can improve final product quality, cost effectiveness while reducing anomalous situations that potentially may cause a line stopping. The present paper describes the research background that has driven the design and development of the Self-Learning methodology and reference architecture as the foundation for a new generation of monitoring and control solutions. Furthermore, a real application scenario from the shoe industry is also described to demonstrate the applicability of the proposed solution.

AI and Learning Systems - Industrial Applications and Future Directions

2021

in Sweden. He leads the SOFIA research group (Simulation and Optimization for Future Industrial Applications) and is the Head of Research Education for Energy & Environmental Engineering. He has been the Principal Investigator of a large number of national and international research projects related to automation in the energy and process industry. Among others, he has been the Chief Engineer for the 5.75mEuro project FUDIPO funded by the European Commission. Prior to coming to MDH, he worked for Rolls-Royce plc in the United Kingdom. He has co-authored over 140 peer-reviewed publications and currently supervises 15 doctoral candidates and is the Chair of the ASME/IGTI Aircraft Engine Committee.