For complex industrial processes with multiple operating conditions, it is important to develop effective monitoring algorithms to ensure the safety of the producing processes. This paper proposes a novel monitoring s...
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For complex industrial processes with multiple operating conditions, it is important to develop effective monitoring algorithms to ensure the safety of the producing processes. This paper proposes a novel monitoring strategy based on fuzzy c-means (FCM). First, the high dimensional historical data are transferred to a low dimensional subspace space by locality preserving projection (LPP). Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operating mode. After that, the distance statistics of each cluster are integrated though the membership values into a novel BID monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman (TE) benchmark process.
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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Dear editor,In the industrial processes, timely detection of key quality variables is very important for tracking the product quality, monitoring the process status, and achieving stable and reliable control. However,...
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Dear editor,In the industrial processes, timely detection of key quality variables is very important for tracking the product quality, monitoring the process status, and achieving stable and reliable control. However, the key quality variables are difficult to measure or have obvious time delay. The process
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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The reaction mechanism of chemical process 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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This paper presented a simple method of detecting the peak of R wave in Electrocardiogram (ECG) signal and computing the Heart Rate Variability (HRV). Features were extracted from the obtained HRV to analyze the vigil...
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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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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.
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.
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.
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