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EO for Systems Modeling and Control
Published in Yong-Zai Lu, Yu-Wang Chen, Min-Rong Chen, Peng Chen, Guo-Qiang Zeng, Extremal Optimization, 2018
Yong-Zai Lu, Yu-Wang Chen, Min-Rong Chen, Peng Chen, Guo-Qiang Chen
There are many optimization problems in industrial process control, for example, system identification, controller parameter tuning, and online/real-time optimization. The rapid growth and huge progress in manufacturing business environment and technology leads to new requirements for control technology and system science. The primary mission of process control has extended from stability, safety, and quality to constrained optimization control for desired KPI (key performance index). As one of the popular advanced process control (APC) solutions, the model predictive control (MPC) has been widely applied in process industries. However, with serious uncertainty and competition in the global marketplace, a manufacturing enterprise faces critical challenges in making its business and production more flexible and dynamic. In other words, a manufacturing enterprise should be able to maximize its profits and provide customers with high-quality products/services under varying marketplace conditions. The issues mentioned above involve different types of optimization problems. Eventually, the traditional modeling, optimization, and control approaches could hardly be applied in solving those complex systems with highly nonlinear and highly uncertain natures. All those issues will encourage researchers and practitioners to devote themselves to the development of novel methods and new technology that is suitable for applications in process automations.
Digital technology trends and their implementation in the mining industry
Published in Christoph Mueller, Winfred Assibey-Bonsu, Ernest Baafi, Christoph Dauber, Chris Doran, Marek Jerzy Jaszczuk, Oleg Nagovitsyn, Mining Goes Digital, 2019
The algorithm for analyzing the onemine datatbase were executed on a 4 cores @ 3.4 GHz and 32 GB machine and took in the average 25 min. In total 2400 paper were analyzed and 25 individual trends in 164 paper were recognized. In the filtered papers, proper name lists were created and 92 papers with a possible mine name citation were detected. By reviewing the papers again manually, 65 papers with 158 mining operations were identified. The TO from all papers are shown in Figure 5. Especially “automation”, “real time data”, “advanced process control” and “machine learning” show high values.
Smart Manufacturing
Published in Tugrul Daim, Marina Dabić, Yu-Shan Su, The Routledge Companion to Technology Management, 2023
The causal diagram of Smart production is shown in Figure 31.3. The Smart production causal diagram shows that B2 “Machine learning” points to B3 “Advanced process control” and B4 “Smart machines.” B3 “Advanced process control” and B4 “Smart machines” point to each other. B1 “Establish smart production standards” does not affect other constructs and is not affected by other constructs.
Adaptive optimization of heating curves in buildings heated by a weather-compensated heat pump
Published in Science and Technology for the Built Environment, 2019
PrimoŽ PotoČnik, Edvard Govekar
Considerable improvements in thermal comfort can be achieved by using advanced process control techniques, such as model predictive control (MPC) (Serale et al. 2018; Lindelöf et al. 2015), where the optimization system consists of a thermal model of the building, weather forecasts, various constraints, and an optimization algorithm. The benefits of applying MPC to building services have been broadly investigated in a number of studies (Smarra et al. 2018; Schmelas et al. 2017). Although good results have been reported when using a rather simple first order house model for the MPC of a domestic heat pump (Kajgaard et al. 2013), for the effective operation of MPC, it is the precision and accuracy of the thermal building models that is the most important (Prívara et al. 2013). There are three general categories of thermal building models: detailed physical models or so-called white-box models, simplified physical or grey-box models, and statistically-based models (Li and Wen 2014). In many of today's building model predictive applications, smart on-line optimization is not yet fully available due to the high complexity of the system, or due to the associated costs. For this reason simple and cost-efficient solutions need to be developed in order to gain access to broader applications in modern buildings, such as the simplified self-learning predictive control approach (Thieblemont et al. 2018).
Application of Industry 4.0 tools to empower circular economy and achieving sustainability in supply chain operations
Published in Production Planning & Control, 2023
Surajit Bag, Lincoln C. Wood, Arnesh Telukdarie, V. G. Venkatesh
I4.0 employs an advanced process control which includes tools such as Model predictive control, Optimisation, Simulation, Statistical process control, Run2Run and Fault detection and classification, facilitating firms to strategically plan, manage and control operations effectively (Telukdarie et al. 2018).