Author
Hao Wang
Bio: Hao Wang is an academic researcher from Seoul National University. The author has contributed to research in topics: Nonlinear system & Process control. The author has an hindex of 1, co-authored 2 publications receiving 34 citations.
Papers
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TL;DR: In this paper, a nonlinear predictive control framework is presented, in which nonlinear processes are modeled using neural networks, with the emphasis placed on the convergence of neural networks to desired steady states.
35 citations
TL;DR: In this article, the authors present an efficient, practical gain scheduled controller for a nonlinear process of Wiener type, pH problem, using locally linearized first-order models to approximate the real process in the neighborhood of steady points, thus resulting in a conventional PI controller within IMC framework.
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TL;DR: Certain types of neural networks that have proved to be effective in practical applications are described, the advantages and disadvantages of using them are mentioned, and four detailed chemical engineering applications are presented.
Abstract: A growing literature within the field of chemical engineering describing the use of artificial neural networks (ANN) has evolved for a diverse range of engineering applications such as fault detection, signal processing, process modeling, and control. Because ANN are nets of basis functions, they can provide good empirical models of complex nonlinear processes useful for a wide variety of purposes. This article describes certain types of neural networks that have proved to be effective in practical applications, mentions the advantages and disadvantages of using them, and presents four detailed chemical engineering applications. In the competitive field of modeling, ANN have secured a niche that now, after one decade, seems secure.
235 citations
TL;DR: This approach was applied in some industrial chemical process: the process of nylon-6,6 polymerization in a twin-screw extruder reactor and an acetic anhydride plant.
177 citations
TL;DR: In this article, a universal method to create a family of neural models, useful for the reactor and reacting system of any type, has been elaborated and presented based on this method a detailed analysis of the neural models has been performed.
Abstract: New aspects of neural modelling of chemical reactors have been investigated in this study An universal method to create a family of neural models, useful for the reactor and reacting system of any type, has been elaborated and presented Based on this method a detailed analysis of the neural models has been performed The proposed methods of modelling as well as a comparative analysis of the obtained results have been illustrated with the data obtained for a complex, catalytic hydrogenation of 2,4-dinitrotoluene performed at non-steady state conditions in a multiphase stirred tank reactor The methods of choosing the input–output signals, the net architecture, the learning method, the number and quality of learning data have been proposed and their influence on the accuracy of obtained predictions have extensively been discussed A comparison of two types of neural models: a global neural model and a hybrid neural model to a conventional reactor modelling has been performed General conclusions and useful criteria have been formulated
65 citations
TL;DR: Approximate importance of the various input variables on the output variables was calculated based on the partitioning of connection weights which showed that bottoms temperature, overhead composition and overhead temperature are not much affected by the disturbances in feed rate, feed composition and vapor rate in the given range.
Abstract: Artificial neural networks can be used as a fault diagnostic tool in chemical process industries. Connection strengths representing correlation between inputs (sensor measurements) and outputs (faults) are made to learn by the network using the back propagation algorithm. Results are presented for diagnostic faults in an ammonia–water packed distillation column. First, a 6-4-6 network architecture (six input nodes corresponding to the state variables and six output nodes corresponding to the six malfunctions) was chosen based on the minimum root-mean-square-error and mean absolute percentage error; and a maximum value of the Pearson correlation coefficient (CP). The values of the learning rate, momentum and the gain terms were taken as 0.8, 0.8 and 1.0, respectively. The detection of the designated faults by the network was good. Relative importance of the various input variables on the output variables was calculated based on the partitioning of connection weights which showed that bottoms temperature, overhead composition and overhead temperature are not much affected by the disturbances in feed rate, feed composition and vapor rate in the given range. This resulted in a simplified 3-4-6 net architecture with similar capabilities as the 6-4-6 net thereby reducing the number of computations.
49 citations
TL;DR: A new control technique for nonlinear control based on hybrid neural modeling is proposed where a variant of the well-known gradient steepest descent method is employed where the learning rate is adapted in each iteration step in order to accelerate the speed of convergence.
39 citations