Novel Approaches to Online Process Optimization Under Uncertainty: Addressing the limitations of current industrial practice
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In the face of growing competition and increased necessity to focus on sustainability and energy efficiency, there is a clear need to optimize the day-to-day operation of many industrial processes. This involves online real-time decision making. One strategy for online decision-making is to use model-based real-time optimization (RTO). Despite the motivation and the potential, real-time optimization is not as commonly used in practice as one would expect. This thesis deals with the research question: Why is traditional real-time optimization not commonly used in industry, and how can these challenges be addressed to facilitate autonomous decision-making? Autonomous decision-making requires real-time plant data from the sensor measurements. However, in many industrial applications, large chunks of data are often discarded and not used effectively in the decision-making process. In part I of the thesis, I propose different novel algorithms that effectively uses real-time plant data for real-time decision-making. In addition to the real-time data, optimal decision-making also requires accurate models that describes the plant. Developing such accurate models are time consuming, costly and requires expert knowledge. To address this issue, I also present a systematic approach on how to translate the economic objectives into meaningful control objectives, that can be achieved using simple tools. Solving optimization problems online can be computationally intensive, even with today’s computing power. To address this issue, I present novel methods on how to address the computational cost of solving optimization problems, in Part II of the thesis. In addition to the novel algorithms and methods presented, one of the main contributions of this thesis is that, it presents an overview of online process optimization, and provides useful discussions on optimization problem formulation under uncertainty that takes in to account the industrial needs and human factors, which are essential to address the limiting factors with current industrial practice.