Shankar Narasimhan S

The problem of selecting the variables to be measured in order to maximize process reliability was tackled in our previous articles (Ali and Narasimhan, 1993, 1995). In this article, this approach is extended to the optimal design of sensor networks for bilinear processes. Diverse processes, such as a mineral beneficiation plant, a separation system of a synthetic juice plant, and a crude preheat train of a refinery are used to illustrate the utility of this approach.

The data validation consists in obtaining an estimation of the true values of process variables that respect the balance equations. Generally, the procedure needs the knowledge of the variance of the measurement errors; unfortunately, in most situations. we only have a rough estimation of this variance and therefore the data validation procedure gives results depending on this poor estimation. A pioneer work of Almasy and Mah (1984) presents a solution to this problem based on the analysis of the constraint residuals. Darouach et al. (1989) developed a slighty different approach based on a maximum Iikelihhod estimator. Here we present a direct method that simultaneously estimates the variances of the measurement errors and reconciles the data with respect to the balance equations. Some numerical results illustrate the efficiency of the proposed method. © 1999 Elsevier Science Ltd.

An algorithm is presented for computing the water surface profiles in steady-state gradually varied flows in tree-type open-channel networks. The algorithm is based on the principles of (1) decomposing the channel network into units that are as small as possible; (2) solving the smaller units using an appropriate method, such as the fourth-order Runge-Kutta method; and (3) connecting the solutions for the smaller units to obtain the final solution for the whole network using the Shooting Method. Elementary graph theoretical concepts are utilized to choose the iterative flow variables so that the small units can be solved efficiently. The algorithm is computationally more efficient than the direct method using the Newton-Raphson technique by an order of magnitude. It does not involve the solution of large matrix equations. The efficiency of the algorithm is illustrated by solving an example tree-type channel network with 42 nodes, 41 channels, and a total of 429 grid points. The proposed method will also be very useful in the design and optimization of tree-type channel networks.

In this study, a nonlinear programming approach using the successive quadratic programming optimization technique is developed for the optimal design of a pipeline network for water supply systems. The proposed method eliminates the equality constraints describing the hydraulics by a suitable choice of dependent and independent variables. The dependent variables are chosen based on graph theoretic decomposition of the network structure. This makes it possible to compute analytically the reduced constraints, objective function gradients, and reduced Hessian in a very efficient manner. This method of decomposition ensures that the nodal and loop balances are exactly satisfied and is robust for any initial starting point, able to handle incorrect initial flow directions. The method gives solutions comparable to the previous optimal solutions for the design of new as well as expansion of existing water distribution networks. ©ASCE.

Gross error detection (GED) is an important function in automated processing of plant data. All GED tests developed so far are based on a linear theory and can be applied to nonlinear processes only after suitable linearization of the process constraints. In this paper, we propose a test for GED in nonlinear processes which does not require the constraints to be linearized. Although the proposed test does not have a rigorous statistical basis, it is entirely analogous to the generalized maximum likelihood ratio test. This test is combined with different existing strategies for multiple GED to determine the best possible method. Simulation results show that for a significantly nonlinear system the proposed test performs better than tests which rely on linearizing the constraints. However, for mildly nonlinear systems such as those with only bilinear constraints, the performances are comparable. The simple serial compensation strategy is shown to be better than its modified version as well as the serial-elimination strategy, especially when the aim is to maximize accuracy of the final estimates.