Risk-aware model based control and applications on whey separation processes
Başlık çevirisi mevcut değil.
- Tez No: 543176
- Danışmanlar: Yrd. DOÇ. Dr. LEYLA ÖZKAN
- Tez Türü: Doktora
- Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2018
- Dil: İngilizce
- Üniversite: Technische Universiteit Eindhoven
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 290
Özet
.
Özet (Çeviri)
Because of the available computational power, processes that manifest complex behavior can be represented with large-scale and nonlinear models containing differential-algebraic equations. These models are able to represent the physical phenomena for a wide range of operating conditions by using the conservation laws and the physical/geometric structure of the process. Hence, one general goal for current model based applications is to incorporate these rigorous first principles based models into design and daily operation of processes, which is in direct conflict with the computational complexity constraints caused by the realtime operation requirements. Over the last few decades, model predictive control (MPC) algorithms have become an accepted control approach in the process industry. In control applications, MPC technology achieves desired quality specifications on the outputs by making use of the future predictions of the system evolution. These predictions are generated from a mathematical model. Although increasingly better models are being developed, the true process behavior always differs from the predictions due to disturbances, unmodelled dynamics or unexpected changes in the upstream. To overcome the detrimental effects, one needs to incorporate elements from uncertain future into the predictions. With these predictions, by solving an optimization problem in every decision instant, one can achieve optimal operation, thus higher savings in costs and less utilization of resources, while adhering to the physical or economic constraints. By this way, we effectively control processes for some (or all) possible uncertain elements. The first part of this dissertation, covered in Chapters 2-3-4-5, addresses the problem of synthesizing computationally tractable and stochastically robust predictive controllers based on large-scale models. To achieve a risk-averse controller, centralized moments of the uncertain predictions are incorporated into the predictive control problem. The dissertation first presents analysis results on the effect of various descriptions of moment based MPC algorithms constructed for different classes of uncertainties. Detailed simulation results are used to discuss the effect of design parameters on the time and frequency domain characteristics of the closed-loop system. Furthermore, for risk-aware MPC problems, we provide explicit reformulation (tightening) of constraints for various classes (bound, affine or quadratic) of constraints. Our results indicate that, an uncertainty-free predictive control problem with reformulating several optimization parameters can guarantee robust operation even if the unknown effects are present in the operation. Furthermore, one should quantify and model the uncertain effects to the finest detail which reflects in the control law formulation. By this way, one can reduce the unnecessary pessimism induced for guaranteeing robust operation. The second topic that is discussed in this dissertation, Chapters 6-7-8, is on the modelling, scheduling, monitoring and (low level and batch-to-batch) control of a whey protein separation process. Whey contains high levels of proteins that are extracted via (membrane) separation process apart from other necessary unit operations. A control relevant model of ultrafiltration (UF) membrane units is proposed and validated with the operation data gathered from an industrial plant. The presented UF membrane model uses the series of resistance concept to describe the fouling phenomena, which is the main cause of performance deterioration in separation processes. Through the use of this model, one can track the fouling, and therefore improve the operation efficiency by adjusting the inlet variables accordingly for better operating strategies of UF membranes. We demonstrate that one needs to distribute the required filtration amount among different membrane stages to decrease the accumulation of fouling. By using data based techniques, observability and identifiability analysis of the model is also reported. This allows the practitioners to select sensors to gather measurements, or saved simulation data, that yield the highest information content about the system at hand. Another conducted simulation study is the comparison between the classical and advanced (model-based) control structures. A crucial discussion presented in the dissertation is the learning aspect that has been incorporated into the model-based controllers, meaning that across the distinct batches of operation, we are able to improve the control performance, by using the errors observed in the past batches. Lastly, we provide (high-level) optimal operating schedules of unit operations and the optimal input trajectories for the whey processes.
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