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The real time optimization (RTO) of gold cyanidation leaching process has been investigated in this paper. To solve inevitable plant-model mismatch and process disturbance in practice, an RTO strategy based on SCFO (Sufficient Conditions for Feasibility and Optimality) for gold cyanidation leaching process is proposed, where the SCFO are used to enforce the desirable properties (both feasibility and optimality) of the solutions resulted from the traditional RTO methods. The simulation results show that compared with the traditional RTO methods, there is a significant improvement of production cost by enforcing the SCFO under model-parameter uncertainty, model-structure uncertainty and unknown process disturbance, which has laid an important foundation for the successful implementation of the plant-wide optimization and control for hydrometallurgy process.

This paper develops a batch-to-batch iterative learning control (ILC) strategy based on online sequential extreme learning machine (OS-ELM) for batch optimal control. On the basis of extreme learning machine (ELM), a data-based nonlinear model is first adopted to capture the batch process characteristics aiming to obtain superior predictive accuracy. Subsequently, due to the model−plant mismatch in real batch processes, an ILC algorithm with adjusting input trajectory by means of error feedback is employed focusing on the improvement of the final product quality. In order to cope with the problems of the unknown disturbances and process variations from batch to batch, when a batch run is completed, OS-ELM is utilized to update the model weights so as to guarantee the precision of the model for optimal control, which corresponds to a nonlinear updating procedure. The feasibility and effectiveness of the proposed method are demonstrated via the application to a simulated bulk polymerization of the styrene b...

Gold cyanidation leaching process is a complicated chemical process. Establishing an accurate process model is of important significance for control and optimization of leaching process. By analyzing the inherent characteristics of leaching process, two types of dynamic hybrid models are designed in this paper, which consist of mass conservation equations as the dynamic mechanistic model and BP neural networks that are used to compensate for the difference between the model output and the real output or to estimate the unknown parameters. For the serial hybrid model, because of the immeasurability of kinetic reaction rates, the Tikhonov regularization method is used to estimate the kinetics reaction rates of gold and cyanide ion, which reduces the propagation of errors in the measured data effectively. The simulation results show that the hybrid models have superior predictive performance than that of the pure mechanistic model. Among the three models, the serial hybrid model has the best predictive precision. The serial hybrid model can predict the concentration of both components accurately, which is an important model basis for subsequent control and optimization of the leaching process.

An efficient improved multi-objective particle swarm optimization algorithm based weighted pheromone sharing mechanism (PM-MOPSO) approach for solving the power supply curve of electric arc furnace(EAF) steelmaking process is presented in this paper. In PM-MOPSO algorithm, the weighted pheromone sharing mechanism coordinates specific gravity among the optimal solutions; the position migration accelerates algorithm convergence speed; the clustering population compression maintains population diversity. Finally, the application shows that it reduces the electric energy consumption, shortens smelting time and improves lifetime of the furnace lining and cover. The result expresses that the algorithm is effective.

This paper deals with the problem of active fault-tolerant control design for a class of linear systems with time-delays. For a class of linear time delay system, firstly, adaptive observer was designed to estimate fault, then a new active fault-tolerant controller is designed which can guarantee that the controlled system keep stable in faulty status, thus the active fault-tolerant control design for this systems is realized. Finally, A numerical example is given to illustrate the efficiency of the proposed design method.

This paper presents an approach based on Projection Pursuit and fuzzy rule extraction combining new hybrid method of classification system. This method is the first to use projection pursuit technology to deal with training set of sample dimensionality reduction and in accordance with the sample classification. According to the results of the classification and the best value projection, using trapezoid distribution method extraction fuzzy rules, producing three types of fuzzy membership function. Finally, in accordance with Fuzzy nearness value to determined the sample under the credit level. The results of experiment showed this method of classification accuracy than the traditional credit evaluation techniques have markedly improved, the reliability is better, has a good application value.

Mixed-media advertising which has become more effective and popular attracts the attention of advertising agencies, media planners and advertisers. How to decide and allocate the budget for mixed-media advertising becomes a fundamental part of a company's advertising campaign.In this paper, an optimization approach based on Genetic Algorithm is proposed to deal with this advertising budget problem which is formulated as a constrained nonlinear programming problem. Then an experiment example is given to explain the process of our approach. The result of this example supports the feasibility and efficiency of our method.