Most of industrial model-based predictive control algorithms are suffering from heavy computation burden when solving QR optimizations and on-line matrixes multiplication within a limited sampling *** order to optimiz...
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Most of industrial model-based predictive control algorithms are suffering from heavy computation burden when solving QR optimizations and on-line matrixes multiplication within a limited sampling *** order to optimize this problem,a fast algorithm is proposed,which the real-time values are modulated into bit streams to simplify the multiplication as the bit based operation could extremely decrease the compute *** addition,the control variables are deduced from the prediction horizon to the current control actuation approximately by a recursive relation instead of figuring all of the control actuations out strictly to reduce the matrix dimension.
In this paper, a relay-feedback PID auto-tuning method and applications related to conventional Wastewater Treatment Plants are presented. The developed method has two steps: identification of the process model parame...
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The ethylene cracking furnace system convert a variety of hydrocarbon feeds into products such as ethylene and propylene in parallel. During the cracking process, the coke produced by the cracking will degrade the per...
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The ethylene cracking furnace system convert a variety of hydrocarbon feeds into products such as ethylene and propylene in parallel. During the cracking process, the coke produced by the cracking will degrade the performance of the cracking furnace, so the furnace should be cleaned periodically. Scheduling of the cracking furnace system involves various raw materials, multiple furnaces, and multiple constraints, so it is a difficult mathematic programming problem. Previous studies have simplified the optimization model to obtain a corresponding mixed integer nonlinear programming(MINLP) model. In order to make the scheduling more suitable for the actual process, this paper develops the transfer-line exchanger(TLE) model, fuel consumption model, and super-high pressure steam(SS) model by using the actual plant data. A case study is put forward to demonstrate the effectiveness of the proposed method.
This paper applies the recently developed framework for integral control on nonlinear spaces to two non-standard cases. First, we show that perfect target stabilization in presence of actuation bias holds also if this...
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A three stage equilibrium model is developed for coal gasification in the Texaco type coal gasifiersbased on Aspen Plus to calculate the composition of product gas, carbon conversion, and gasification teml^erature. Th...
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A three stage equilibrium model is developed for coal gasification in the Texaco type coal gasifiersbased on Aspen Plus to calculate the composition of product gas, carbon conversion, and gasification teml^erature. The model is divided into three stages including pyrolysis and combustion stage, char gas reaction stage, and gas *** reaction stage. Part of the water produced in thepyrolysis and combust!on stag.e is assumed to be involved inthe second stage to react with the unburned carbon. Carbon conversion is then estimated in the second stage by steam participation ratio expressed as a function of temperature. And the gas product compositions are calculated from gas phase reactions in the third stage. The simulation results are consistent with published experimental data.
Aiming at the disadvantages of the standard Particle Swarm optimization (PSO), a new particle swarm optimization algorithm based on dual mutation(DDPSO) is proposed. By comparing and analyzing the results of several B...
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The reaction mechanism of chemicalprocess is complex. There is a modeling error between the mechanism model and the actual reaction system. At the same time, there are complex slow time-varying features, such as cata...
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In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation fa...
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In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.
In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new...
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In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.
Two general approaches are adopted in solving dynamic optimization problems in chemicalprocesses, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been w...
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Two general approaches are adopted in solving dynamic optimization problems in chemicalprocesses, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution (DE) algorithm and control vector parameteri- zation (CVP) is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.
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