Browsing by Author "Yuan, Xiaofeng"
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Item Open Access Global approximation of self-optimizing controlled variables with average loss minimization(American Chemical Society, 2015-11-23) Ye, Lingjian; Cao, Yi; Yuan, XiaofengSelf-optimizing control (SOC) constitutes an important class of control strategies for real-time optimization (RTO) of chemical plants, by means of selecting appropriate controlled variables (CVs). Within the scope of SOC, this paper develops a CV selection methodology for a global solution which aims to minimise the average economic loss across the entire operation space. A major characteristic making the new scheme different from existing ones is that each uncertain scenario is independently considered in the new solution without relying on a linearised model, which was necessary in existing local SOC methods. Although global CV selection has been formulated as a nonlinear programming (NLP) problem, a tractable numerical algorithm for a rigorous solution is not available. In this work, a number of measures are introduced to ease the challenge. Firstly, we suggest to represent the economic loss as a quadratic function against the controlled variables through Taylor expansion, such that the average loss becomes an explicit function of the CV combination matrix, a direct optimizing algorithm is proposed to approximately minimize the global average loss. Furthermore, an analytic solution is derived for a suboptimal but much more simplified problem by treating the Hessian of the cost function over the entire operating space as a constant. This approach is found very similar to one of existing local methods, except that a matrix involved in the new solution is constructed from global operating data instead of using a local linear model. The proposed methodologies are applied to three simulated examples, where the effectiveness of proposed algorithms are demonstrated.Item Open Access Retrofit self-optimizing control of Tennessee Eastman process(Institute of Electrical and Electronics Engineers, 2016-05-19) Ye, Lingjian; Cao, Yi; Yuan, Xiaofeng; Song, ZhihuanThis paper considers near-optimal operation of the Tennessee Eastman (TE) process by using a retrofit self-optimizing control (SOC) approach. Motivated by the factor that most chemical plants in operation have already been equipped with a workable control system for regulatory control, we propose to improve the economic performance by controlling some self-optimizing controlled variables (CVs). Different from traditional SOC methods, the proposed retrofit SOC approach improves economic optimality of operation through newly added cascaded SOC loops, where carefully selected SOC CVs are maintained at constant by adjusting set-points of the existing regulatory control loops. To demonstrate the effectiveness of the retrofit SOC proposed, we adopted measurement combinations as the CVs for the TE process, so that the economic cost is further reduced comparing to existing studies where single measurements are controlled. The optimality of the designed control architecture is validated through both steady state analysis and dynamic simulations.Item Open Access Subset measurement selection for globally self-optimizing control of Tennessee Eastman process(Elsevier, 2016-08-09) Ye, Lingjian; Cao, Yi; Yuan, Xiaofeng; Song, ZhihuanThe concept of globally optimal controlled variable selection has recently been proposed to improve self-optimizing control performance of traditional local approaches. However, the associated measurement subset selection problem has not be studied. In this paper, we consider the measurement subset selection problem for globally self-optimizing control (gSOC) of Tennessee Eastman (TE) process. The TE process contains substantial measurements and had been studied for SOC with controlled variables selected from individual measurements through exhaustive search. This process has been revisited with improved performance recently through a retrofit approach of gSOC. To extend the improvement further, the measurement subset selection problem for gSOC is considered in this work and solved through a modification of an existing partially bidirectional branch and bound (PB3) algorithm originally developed for local SOC. The modified PB3 algorithm efficiently identifies the best measurement candidates among the full set which obtains the globally minimal economic loss. Dynamic simulations are conducted to demonstrate the optimality of proposed results.