Because industrial process is often under changes, it is necessary to reconcile the measurement of the dynamic process. There are many problems in the present dynamic data reconciliation methods, such as low calculation efficiency, difficulty of treatment for the reconciliation of input variables. Based on the analysis of the characteristic of dynamic data reconciliation, an integral approach is presented which integrates finite element collocation method, filtering technique and robust method. The finite element collocation method can reduce the amount of discrete model constrained equations to decrease the problem complexity without any loss of measurement information. The filtering technique can eliminate random errors in input variables effectively and no lag or signal distortion will be introduced. Monte Carlo method is used to test the efficiency of the robust method for gross error detection. The calculation results show that the integral approach can improve the calculation efficiency greatly- and can deal with the gross error of abnormal type properly.

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.

A strategy of developing on-line optimization intelligent systems based on combining flo wsheeting simulation and optimization package with artificial neural networks (ANN) is presented in this paper. A number of optimization cases for a certain chemical plant are obtained off-line by using PROCESS-Ⅱ or other flowsheeting programming with optimization. Then, taking these cases as training examples, we establish a neural network systems which can be used on-line as an optimizer to obtain setpoints from input data sampled from distributed control system through gross errordetecton and datareconciliation procedures./Such an on-line optimizer possesses two advantages over nonlinear programming package: first of all, there is no convergence problem for the trained ANN to be used online; secondly, the frequency for setpoints updating is not limited because only algebraic calculation rather than optimization is required to be carried out on-line. Here two key problems of implementing ANN approaches to the on-line optimization are discussed: how to improve the prediction accuracy of ANNs models for meeting the optimization requirements. Results from an actual fractionation unit of a FCC plant in a refinery showed a 0.5%-1.0% increase in the total recovery of light oil products. Details of the strategy used are described.

A knowledge-based system for aiding in the construction of practical heat exchanger networks called SPHEN is presented in this article. The created network configurations feature low utility cost, low capital investment, rather stabie heat recovery during operation and rational distribution of pressure drops among the network branches. SPHEN consists of four component parts: network construction, simulator, flowsbeet diagram generation and user's feedback subsystem. So SPHEN is characterized by its hybridity, reflected in knowledge representation and abstraction, solving methods and implementation languages. The hybrid system is implemented through disk files and data files connecting different parts into one working space. An example of redesigning an energy recovery network of a large-scale crude oil unit is illustrated and the results obtained are successful.

In two related papers we define the concept of structural flexlbility and apply it specifically to a class of heat integrated distillation processes. A process ts structurally feasible if the type of equipment it contains and how it is currently configured allow it to meet specified design requirements. It is structurally infeasible if it does not. Size of equipment is not at issue. The first paper develops a simple rnethod to permit an estimate to be made of a structural flexibility index for a heat integrated distillation process with a fixed configuration. Multieffect columns are permitted. This work shows, that processes that differ only in their heat integration can have very different indices for structural flexibility. The second paper presents a synthesis metfiodotogy to discover the heat integrated arrangements which maximize the structural flexibility index for the process, Only the simpler arrangements of such columns are considered where they are placed one above the other and without multieffecting. The papers present algorithms by example. Insights are stressed.