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The electric power demands of many petrochemical plants are matched by supplies from an in-house cogeneration system and from the electric grid. However, due to the fluctuations of fuel costs, production, and electricity rates, it is necessary to balance electric supply between these two sources. In reality, uncertain effects play a very important role in this decision-making problem. One of the most important uncertainties is the occurrence of power interruptions from either one of the supply sources, which could endanger operability and reliability of plant operations. To minimize the total energy cost under consideration of unexpected power failures, we break up the solution of the problem into two layers. The outer layer is to determine the optimum contracting of three-section time-of-use rate. We use an artificial neural network regression model as a meta-model to simulate the contour plot of a nonconvex cost function. The occurrences of incidental power failures are simulated by the Monte Carlo method. The inner layer is to determine the optimum operation of the cogeneration system. Since the searching space is huge in the outer layer and the Monte Carlo simulation in the inner layer is time consuming, we implement an interactive sampling search approach to find the optimal contract capacity in this multi-local-optima problem. Index Terms--Cogeneration, meta-model approach, Monte Carlo simulation, optimal contract, uncertainty factors.

Combustion in a boiler is too complex to be analytically described with mathematical models. To meet the needs of operation optimization, on-site experiments guided by the statistical optimization methods are often necessary to achieve the optimum operating conditions. This study proposes a new constrained optimization procedure using artificial neural networks as models for target processes. Information analysis based on random search, fuzzy c-mean clustering, and minimization of information free energy is performed iteratively in the procedure to suggest the location of future experiments, which can greatly reduce the number of experiments needed. The effectiveness of the proposed procedure in searching optima is demonstrated by three case studies: (1) a bench-mark problem, namely minimization of the modified Himmelblau function under a circle constraint; (2) both minimization of NOx and CO emissions and maximization of thermal efficiency for a simulated combustion process of a boiler; (3) maximization of thermal efficiency within NOx and CO emission limits for the same combustion process. The simulated combustion process is based on a commercial software package CHEMKIN, where 78 chemical species and 467 chemical reactions related to the combustion mechanism are incorporated and a plug-flow model and a load-correlated temperature distribution for the combustion tunnel of a boiler are used. [Copyright &y& Elsevier]