A wavelet-based method is proposed for steady-state detection in continuous processes. In this method, process trends are extracted from the measured raw data via wavelet-based multi-scale processing. The process status is then measured using an index with value ranging from 0 to 1 according to the wavelet transform modulus of the extracted process signal. Finally, a steady state is identified if the computed index is small (close to zero). The determination of a characteristic scale for performing steady-state detection was also studied. Compared with the existing approaches for steady-state detection, this method has better precision for detecting changes in process due to the good localization property of wavelet transform, and is more suitable for on-line applications. In this paper, the method is described in detail, and has then been applied to the crude oil unit of a refinery, and to the recausticizing plant of a chemical pulp mill.

Dynamic data reconciliation can supply more accurate data for dynamic optimization, dynamic fault diagnosis, and control by means of incorporating process information in some mathematical model. It will be an ill-posed inverse problem if the sensitive input variables are unmeasured; here, the sensitive input variable is defined as the variable that, if it is unmeasured, can only be estimated through the differentiation of other measured variables. In such a case, existing methods cannot obtain correct and usable data effectively. To address the problem, based on the principle of regularization, the wavelets are adopted to construct regular operators. And, a new approach is proposed to determine the optimal scale level corresponding to the optimal approximate operator in which the prior statistical information of the signal is utilized. The algorithm can deal with the estimation of unknown sensitive input variable effectively. The results show that more accurate estimation of the sensitive input variable can be obtained by using the proposed method as compared with the one obtained by using existing collocation methods based on polynomials.

This article addresses a new aspect of the problem of sensor network design, namely the concept of flexibility of sensor network. Algorithms based on graph theoretical concepts and MINLP methods are developed for analyzing the flexibility of a given sensor network, designing a flexible sensor network, and upgrading a sensor network to improve its flexibility. Several examples are reported to illustrate the presented algorithms. Using the proposed approach, one can obtain a flexible sensor network, which is able to ensure the observability of all the key variables even under some cases in which the original flowsheet changes.

In this paper, Wavelet Transform is applied to improve the on-line prediction of crude oil distillation (COD) qualities. First, the wavelets-based multi-scale analysis method was applied to treat the process data effectively. According to their unique characteristics with multi-scale Wavelet Transform, different variations are detected and modified, such as steps, peaks, noises, abnormal sudden changes, and so on. Using the process data with noises discarded and abnormal sudden changes treated effectively, the COD quality prediction can be sure of safety and high accuracy. Next, for some COD units on-line quality analyzers are available, and a new correcting strategy is taken in this paper to give effective update. The update is accomplished based on process data trends extraction. At last, the data processing and the on-line update strategy are introduced to a prediction system of COD product qualities to improve its prediction accuracy. The improved prediction system has been applied in a real plant, and the results are satisfactory.

Dealing with multidimensional problems has been the "bottle-neck" for implementing wavenets to process systems engineering. To tackle this problem, a novel multidimensional wavelet (MW) is presented with its rigorously proven approximation theorems. Taking the new wavelet function as the activation function in its hidden units, a new type of wavenet called multidimensional non-orthogonal non-product wavelet-sigmoid basis function neural network (WSBFN) model is proposed for dynamic fault diagnosis. Based on the heuristic learning rules presented by authors, a new set of heuristic learning rules is presented for determining the topology of WSBFNs. The application of the proposed WSBFN is illustrated in detail with a dynamic hydrocracking process.