The p-xylene (PX) oxidation process is of great industrial importance because of the strong demand of the global polyester fiber. A steady-state model of the PX oxidation has been studied by many researchers. In our p...
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For a class of nonlinear discrete time system with fast time-varying or jumping parameters, a multiple models adaptive controller (MMAC) based on cluster-optimization is proposed. Based on the input-output data, the s...
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This study provides insights into the distillation sequence optimization of refinery system in a methanol to propylene plant with extractive distillation under multiple conditions. The simulated annealing algorithm(SA...
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This study provides insights into the distillation sequence optimization of refinery system in a methanol to propylene plant with extractive distillation under multiple conditions. The simulated annealing algorithm(SA) with relative cost function was used to solve a meaningful optimization problem. It was observed that different conditions had differed on the flowsheet. Case study shows the effectiveness of the proposed method.
Modern chemicalprocesses are usually characterized by large-scale,complex correlation,and strong dynamics,and monitoring of such processes is *** paper proposes a performance-driven fault-relevant dynamic principal c...
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ISBN:
(纸本)9781538629185
Modern chemicalprocesses are usually characterized by large-scale,complex correlation,and strong dynamics,and monitoring of such processes is *** paper proposes a performance-driven fault-relevant dynamic principal component(FRDPC) subspace construction integrated with Bayesian inference method to achieve efficient monitoring for dynamic chemical ***,dynamic principal component analysis is employed to deal with both auto-correlation and cross-correlation among ***,considering fault information has no definite mapping to a certain dynamic principal component(DPC) and the existence of non-beneficial DPCs may cause redundancy in the monitoring,an FRDPC subspace is constructed for each fault through the performance-driven DPC *** new process measurements are examined in each FRDPC subspace as well as the residual *** monitoring results in all subspaces are fused to a comprehensive index through Bayesian inference to provide an intuitive indication of the process *** studies on a numerical example and the Tennessee Eastman benchmark process indicate the efficiency.
This paper proposes a framework for solving high-dimensional robust multi-objective optimization problems. A decision variable classification-based framework is developed to search for robust Pareto-optimal solutions....
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The flow shop scheduling problem with limited buffers is widely existing in manufacturing systems. This article proposes a hybrid discrete harmony search algorithm for the problem to minimize total flow time. The algo...
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The flow shop scheduling problem with limited buffers is widely existing in manufacturing systems. This article proposes a hybrid discrete harmony search algorithm for the problem to minimize total flow time. The algorithm presents a novel discrete improvisation and a differential evolution scheme with the job-permutation-based representation. Moreover, the discrete harmony search is hybridized with the problem-dependent local search based on insert neighborhood to balance the global exploration and local exploitation. In addition, an orthogonal experiment design is employed to provide a receipt for turning the adjustable parameters of the algorithm. Comparisons based on the Taillard benchmarks indicate the superiority of the proposed algorithm in terms of effectiveness and efficiency.
The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is ...
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In a resource limited multi-agent system, it is of practical importance to select a fraction of nodes (agents) to provide control inputs such that consensus can be achieved with optimized performance in terms of netwo...
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Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks...
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Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.
A brain-computer interface (BCI) based on the combination of oddball paradigm and face perception has been introduced. Such BCI mainly exploits three event-related potential (ERP) components, namely vertex positive po...
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