Showing papers in "Computers & Chemical Engineering in 1998"

TL;DR: Several recursive partial least squares (RPLS) algorithms are proposed for on-line process modeling to adapt process changes and off-line modeling to deal with a large number of data samples.

TL;DR: Nonlinear Model Predictive Control (NMPC) as discussed by the authors is the industry standard for controlling constrained multivariable nonlinear processes with a large operating regime, and it is well suited for controlling nonlinear process with constraints.

TL;DR: The deterministic global optimization algorithm, αBB (α-based Branch and Bound) is presented, which offers mathematical guarantees for convergence to a point arbitrarily close to the global minimum for the large class of twice-differentiable NLPs.

TL;DR: In this article, the authors present an overview of current status of control performance monitoring using minimum variance principles, including extensions to PID-achievable performance assessment, tradeoff between performance and robustness, and trade-off between deterministic and stochastic performance objectives are discussed.

TL;DR: The performance of the proposed algorithm and its alternative underestimators is studied through their application to a variety of problems and a number of rules for branching variable selection and variable bound updates are shown to enhance convergence rates.

TL;DR: This contribution presents a methodology for the identification of distributed parameter systems, based on artificial neural network architectures, motivated by standard numerical discretization techniques used for the solution of partial differential equations.

TL;DR: In this paper, an integrated methodology for the design of industrial water systems is proposed based on a decomposition scheme for the optimisation of a superstructure model that includes all the possible features of a design.

TL;DR: A survey of state and parameter estimation and their applications in inferential control and adaptive control can be found in this paper, where the limits and abilities of the methods, the present status of the technological applications, and the need for future advances in the theory, hardware, software, and knowledge transfer will be discussed.

TL;DR: In this article, the authors propose a method to compute the optimal utility system to satisfy the minimum energy requirements of a process at minimum cost, which is a key issue of the energy integration studies.

TL;DR: An automated procedure is used to formulate a constrained non-linear optimization model, whose solution provides a measure of the fitness of each candidate HEN generated by the GA, and the obtained solutions are compared with those available in literature.

TL;DR: A global optimization algorithm is presented to rigorously solve the MINLP model by Yee and Grossmann (1990) for the synthesis of heat exchanger networks under the simplifying assumptions of linear area cost, arithmetic mean temperature difference driving forces and no stream splitting.

TL;DR: In this article, a CFD model for a free bubbling fluidised bed was implemented in the commercial code CFX of AEA Technology, which is based on a two fluid model including the kinetic theory of granular flow.

TL;DR: The framework is based on a two-state stochastic MINLP formulation for the maximization of a function comprising the expected value of the profit, operating and fixed costs of the plant to address process synthesis problems under uncertainty.

TL;DR: In this article, a mixed-integer (MILP) formulation is presented for the synthesis and operational planning of utility systems for multi-period operation with varying demands, which takes into account the investment costs, operating costs of units for all periods, and the planning for scheduled maintenance of equipments.

TL;DR: In this paper, a systematic procedure to select solvents in mass separating agent (MSA) driven technologies so as to account for plant-wide point source or post-release environmental interactions is presented.

TL;DR: A generalized sensor network design algorithm for finding the optimal placement of sensors in a linear mass flow process has been developed and implemented and application to a steam-metering network of a methanol plant demonstrates the versatility of this method.

TL;DR: A physically meaningful HEN structure representation is proposed which can be both effectively manipulated by genetic operators and is also appropriate for parametric optimization by the Simplex algorithm.

TL;DR: In this paper, a hybrid GA algorithm based on the iteration of the GA running parameters followed by the Levenberg-Marquardt optimizer was developed, provided that a proper balance between convergence and diversity was maintained throughout the GA run.

TL;DR: In this article, a nonlinear state feedback controller is computed by exact linearization of the process model to shape the nominal closed-loop system, which is a pure nonlinear feedforward compensator for the nominal plant.

TL;DR: In this paper, a dual temperature simulated annealing approach to bilevel programming problems is presented. But the inner problem is stochastically relaxed with a parameter that can be used as a temperature scale.

TL;DR: In this paper, the authors assess the industrial-scale feasibility of an on-line estimator for state estimation by applying it to an industrial pilot 1m 3 semibatch reactor.

TL;DR: In this paper, a nonlinear planning model for refinery production is developed, which allows the implementation of nonlinear process models as well as blending relations, and a real world application is developed for the planning of diesel production in the RPBC refinery in Cubatao (SP).

TL;DR: The results of on-line optimization of the acetoacetylation of pyrrole with diketene in a laboratory-scale reactor and the minimization of batch time subject to endpoint constraints with respect to yield and two of the concentrations are presented.

TL;DR: In this paper, the authors proposed a fault detection and process monitoring approach using principal component analysis (PCA) for industrial processes, which can represent process as well as sensor faults using a unified geometric approach.

TL;DR: It is shown that the dynamic model, which is in general described by a system of differential-algebraic equations (DAEs), can become high-index during the state-constrained portions of the trajectory.

TL;DR: In this article, the authors present a new model for heat integration that overcomes difficulties experienced either with direct integration approaches, or with the Duran and Grossmann (1986) model when handling isothermal streams.

TL;DR: In this paper, a non-linear PCA method based on the input-training neural network is proposed for monitoring plant performance across a range of industrial processes, and a contribution plot capable of identifying the potential source of the fault in a nonlinear situation is then proposed prior to applying the methodology to a continuous industrial reactor.

TL;DR: In this article, an extended cutting plane method is introduced for solving non-convex mixed-integer non-linear programming problems, although the method was originally introduced for the solution of convex problems only.

TL;DR: In this paper, a series of simulations for a reactive distillation tray column were performed in order to compare the equilibrium and nonequilibrium model, and it was shown that the none-quare model is to be preferred for the simulation of a tray column for reaction of acetic acid and ethanol in production of ethyl acetate.

TL;DR: In this article, a mathematical formulation for the dynamic optimisation of hybrid processes described by general state-transition networks is presented, where transitions occur from one state to another whenever certain logical conditions are satisfied.