Showing papers on "Nonlinear programming published in 1991"

Operations Research: Applications and Algorithms

Abstract: 1. INTRODUCTION TO MODEL BUILDING. An Introduction to Modeling. The Seven-Step Model-Building Process. Examples. 2. BASIC LINEAR ALGEBRA. Matrices and Vectors. Matrices and Systems of Linear Equations. The Gauss-Jordan Method for Solving Systems of Linear Equations. Linear Independence and Linear Dependence. The Inverse of a Matrix. Determinants. 3. INTRODUCTION TO LINEAR PROGRAMMING. What is a Linear Programming Problem? The Graphical Solution of Two-Variable Linear Programming Problems. Special Cases. A Diet Problem. A Work-Scheduling Problem. A Capital Budgeting Problem. Short-term Financial Planning. Blending Problems. Production Process Models. Using Linear Programming to Solve Multiperiod Decision Problems: An Inventory Model. Multiperiod Financial Models. Multiperiod Work Scheduling. 4. THE SIMPLEX ALGORITHM AND GOAL PROGRAMMING. How to Convert an LP to Standard Form. Preview of the Simplex Algorithm. The Simplex Algorithm. Using the Simplex Algorithm to Solve Minimization Problems. Alternative Optimal Solutions. Unbounded LPs. The LINDO Computer Package. Matrix Generators, LINGO, and Scaling of LPs. Degeneracy and the Convergence of the Simplex Algorithm. The Big M Method. The Two-Phase Simplex Method. Unrestricted-in-Sign Variables. Karmarkar"s Method for Solving LPs. Multiattribute Decision-Making in the Absence of Uncertainty: Goal Programming. Solving LPs with Spreadsheets. 5. SENSITIVITY ANALYSIS: AN APPLIED APPROACH. A Graphical Introduction to Sensitivity Analysis. The Computer and Sensitivity Analysis. Managerial Use of Shadow Prices. What Happens to the Optimal z-value if the Current Basis is No Longer Optimal? 6. SENSITIVITY ANALYSIS AND DUALITY. A Graphical Introduction to Sensitivity Analysis. Some Important Formulas. Sensitivity Analysis. Sensitivity Analysis When More Than One Parameter is Changed: The 100% Rule. Finding the Dual of an LP. Economic Interpretation of the Dual Problem. The Dual Theorem and Its Consequences. Shadow Prices. Duality and Sensitivity Analysis. 7. TRANSPORTATION, ASSIGNMENT, AND TRANSSHIPMENT PROBLEMS. Formulating Transportation Problems. Finding Basic Feasible Solutions for Transportation Problems. The Transportation Simplex Method. Sensitivity Analysis for Transportation Problems. Assignment Problems. Transshipment Problems. 8. NETWORK MODELS. Basic Definitions. Shortest Path Problems. Maximum Flow Problems. CPM and PERT. Minimum Cost Network Flow Problems. Minimum Spanning Tree Problems. The Network Simplex Method. 9. INTEGER PROGRAMMING. Introduction to Integer Programming. Formulation Integer Programming Problems. The Branch-and-Bound Method for Solving Pure Integer Programming Problems. The Branch-and-Bound Method for Solving Mixed Integer Programming Problems. Solving Knapsack Problems by the Branch-and-Bound Method. Solving Combinatorial Optimization Problems by the Branch-and-Bound Method. Implicit Enumeration. The Cutting Plane Algorithm. 10. ADVANCED TOPICS IN LINEAR PROGRAMMING. The Revised Simplex Algorithm. The Product Form of the Inverse. Using Column Generation to Solve Large-Scale LPs. The Dantzig-Wolfe Decomposition Algorithm. The Simplex Methods for Upper-Bounded Variables. Karmarkar"s Method for Solving LPs. 11. NONLINEAR PROGRAMMING. Review of Differential Calculus. Introductory Concepts. Convex and Concave Functions. Solving NLPs with One Variable. Golden Section Search. Unconstrained Maximization and Minimization with Several Variables. The Method of Steepest Ascent. Lagrange Multiples. The Kuhn-Tucker Conditions. Quadratic Programming. Separable Programming. The Method of Feasible Directions. Pareto Optimality and Tradeoff Curves. 12. REVIEW OF CALCULUS AND PROBABILITY. Review of Integral Calculus. Differentiation of Integrals. Basic Rules of Probability. Bayes" Rule. Random Variables. Mean Variance and Covariance. The Normal Distribution. Z-Transforms. Review Problems. 13. DECISION MAKING UNDER UNCERTAINTY. Decision Criteria. Utility Theory. Flaws in Expected Utility Maximization: Prospect Theory and Framing Effects. Decision Trees. Bayes" Rule and Decision Trees. Decision Making with Multiple Objectives. The Analytic Hierarchy Process. Review Problems. 14. GAME THEORY. Two-Person Zero-Sum and Constant-Sum Games: Saddle Points. Two-Person Zero-Sum Games: Randomized Strategies, Domination, and Graphical Solution. Linear Programming and Zero-Sum Games. Two-Person Nonconstant-Sum Games. Introduction to n-Person Game Theory. The Core of an n-Person Game. The Shapley Value. 15. DETERMINISTIC EOQ INVENTORY MODELS. Introduction to Basic Inventory Models. The Basic Economic Order Quantity Model. Computing the Optimal Order Quantity When Quantity Discounts Are Allowed. The Continuous Rate EOQ Model. The EOQ Model with Back Orders Allowed. Multiple Product Economic Order Quantity Models. Review Problems. 16. PROBABILISTIC INVENTORY MODELS. Single Period Decision Models. The Concept of Marginal Analysis. The News Vendor Problem: Discrete Demand. The News Vendor Problem: Continuous Demand. Other One-Period Models. The EOQ with Uncertain Demand: the (r, q) and (s,S models). The EOQ with Uncertain Demand: the Service Level Approach to Determining Safety Stock Level. Periodic Review Policy. The ABC Inventory Classification System. Exchange Curves. Review Problems. 17. MARKOV CHAINS. What is a Stochastic Process. What is a Markov Chain? N-Step Transition Probabilities. Classification of States in a Markov Chain. Steady-State Probabilities and Mean First Passage Times. Absorbing Chains. Work-Force Planning Models. 18.DETERMINISTIC DYNAMIC PROGRAMMING. Two Puzzles. A Network Problem. An Inventory Problem. Resource Allocation Problems. Equipment Replacement Problems. Formulating Dynamic Programming Recursions. The Wagner-Whitin Algorithm and the Silver-Meal Heuristic. Forward Recursions. Using Spreadsheets to Solve Dynamic Programming Problems. Review Problems. 19. PROBABILISTIC DYNAMIC PROGRAMMING. When Current Stage Costs are Uncertain but the Next Period"s State is Certain. A Probabilistic Inventory Model. How to Maximize the Probability of a Favorable Event Occurring. Further Examples of Probabilistic Dynamic Programming Formulations. Markov Decision Processes. Review Problems. 20. QUEUING THEORY. Some Queuing Terminology. Modeling Arrival and Service Processes. Birth-Death Processes. M/M/1/GD/o/o Queuing System and the Queuing Formula L=o W, The M/M/1/GD/o Queuing System. The M/M/S/ GD/o/o Queuing System. The M/G/ o/GD/oo and GI/G/o/GD/o/oModels. The M/ G/1/GD/o/o Queuing System. Finite Source Models: The Machine Repair Model. Exponential Queues in Series and Opening Queuing Networks. How to Tell whether Inter-arrival Times and Service Times Are Exponential. The M/G/S/GD/S/o System (Blocked Customers Cleared). Closed Queuing Networks. An Approximation for the G/G/M Queuing System. Priority Queuing Models. Transient Behavior of Queuing Systems. Review Problems. 21.SIMULATION. Basic Terminology. An Example of a Discrete Event Simulation. Random Numbers and Monte Carlo Simulation. An Example of Monte Carlo Simulation. Simulations with Continuous Random Variables. An Example of a Stochastic Simulation. Statistical Analysis in Simulations. Simulation Languages. The Simulation Process. 22.SIMULATION WITH PROCESS MODEL. Simulating an M/M/1 Queuing System. Simulating an M/M/2 System. A Series System. Simulating Open Queuing Networks. Simulating Erlang Service Times. What Else Can Process Models Do? 23. SPREADSHEET SIMULATION WITH @RISK. Introduction to @RISK: The Newsperson Problem. Modeling Cash Flows From A New Product. Bidding Models. Reliability and Warranty Modeling. Risk General Function. Risk Cumulative Function. Risktrigen Function. Creating a Distribution Based on a Point Forecast. Forecasting Income of a Major Corporation. Using Data to Obtain Inputs For New Product Simulations. Playing Craps with @RISK. Project Management. Simulating the NBA Finals. 24. FORECASTING. Moving Average Forecasting Methods. Simple Exponential Smoothing. Holt"s Method: Exponential Smoothing with Trend. Winter"s Method: Exponential Smoothing with Seasonality. Ad Hoc Forecasting, Simple Linear Regression. Fitting Non-Linear Relationships. Multiple Regression. Answers to Selected Problems. Index.

A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Abstract: The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.

Nonlinear control of chemical processes: a review

Abstract: We survey nonlinear control system techniques ranging from ad hoc or process-specific strategies to predictive control approaches based on nonlinear programming. The capabilities of these techniques to handle the common problems associated with chemical processes, such as time delays, constraints, and model uncertainty are discussed. A significant number of goals for future research in nonlinear control of chemical processes are detailed

Submodular functions and optimization

Abstract: Introduction. 1. Mathematical Preliminaries. Submodular Systems and Base Polyhedra. 2. From Matroids to Submodular Systems. Matroids. Polymatroids. Submodular Systems. 3. Submodular Systems and Base Polyhedra. Fundamental Operations on Submodular Systems. Greedy Algorithm. Structures of Base Polyhedra. Intersecting- and Crossing-Submodular Functions. Related Polyhedra. Submodular Systems of Network Type. Neoflows. 4. The Intersection Problem. The Intersection Theorem. The Discrete Separation Theorem. The Common Base Problem. 5. Neoflows. The Equivalence of the Neoflow Problems. Feasibility for Submodular Flows. Optimality for Submodular Flows. Algorithms for Neoflows. Matroid Optimization. Submodular Analysis. 6. Submodular Functions and Convexity. Conjugate Functions and a Fenchel-Type Min-Max Theorem for Submodular and Supermodular Functions. Subgradients of Submodular Functions. The Lovasz Extensions of Submodular Functions. 7. Submodular Programs. Submodular Programs - Unconstrained Optimization. Submodular Programs - Constrained Optimization. Nonlinear Optimization with Submodular Constraints. 8. Separable Convex Optimization. Optimality Conditions. A Decomposition Algorithm. Discrete Optimization. 9. The Lexicographically Optimal Base Problem. Nonlinear Weight Functions. Linear Weight Functions. 10. The Weighted Max-Min and Min-Max Problems. Continuous Variables. Discrete Variables. 11. The Fair Resource Allocation Problem. Continuous Variables. Discrete Variables. 12. The Neoflow Problem with a Separable Convex Cost Function. References. Index.

A combined optimization method for solving the inverse kinematics problems of mechanical manipulators

Abstract: A new method for computing numerical solutions to the inverse kinematics problem of robotic manipulators is developed. The method is based on a combination of two nonlinear programming techniques and the forward recursion formulas, with the joint limitations of the robot being handled implicitly as simple boundary constraints. This method is numerically stable since it converges to the correct answer with virtually any initial approximation, and it is not sensitive to the singular configuration of the manipulator. In addition, this method is computationally efficient and can be applied to serial manipulators having any number of degrees of freedom. >

Nonlinear optimization: complexity issues

Abstract: Optimization and convexity complexity theory convex quadratic programming non-convex quadratic programming local optimization complexity in the black-box model.

Dynamic traffic assignment for urban road networks

Abstract: This paper presents a nonlinear programming formulation of the dynamic user-equilibrium assignment problem (DUE) for urban road networks with multiple trip origins and destinations. DUE is a temporal generalization of the static user-equilibrium assignment problem (SUE) with additional constraints to insure temporally continuous paths of flow. In DUE, the full assignment period of several hours is discretized into shorter time intervals of 10–15 minutes each for which trip departure matrices are assumed to be known. This formulation of DUE includes SUE as a special case in which there is only one time interval for the full assignment period. The assumption of steady-state flows allows SUE to have all linear constraints, but DUE requires nonlinear flow continuity constraints. Whereas SUE is typically solved by methods of linear combinations, these methods create temporally discontinuous flows if applied to DUE. A dynamic traffic assignment heuristic (DTA) is presented that generates approximate solutions to DUE in an efficient manner for large networks. DTA is not a convergent solution algorithm for DUE, but was designed instead to produce assignments that approximate the DUE optimality conditions. An overview of alternative dynamic assignment approaches is given, including the limitations of other optimization and simulation approaches. Test results presented in this paper show that DTA generates both static and dynamic assignments that approximately satisfy the user-equilibrium conditions of these problems.

Algorithms for generalized fractional programming

Abstract: A generalized fractional programming problem is specified as a nonlinear program where a nonlinear function defined as the maximum over several ratios of functions is to be minimized on a feasible domain of źn. The purpose of this paper is to outline basic approaches and basic types of algorithms available to deal with this problem and to review their convergence analysis. The conclusion includes results and comments on the numerical efficiency of these algorithms.

Focalization: environmental focusing and source localization.

Abstract: Conventional matched‐field processing (MFP) requires accurate knowledge of the ocean‐acoustic environment. Focalization, which simultaneously focuses and localizes, eliminates this stringent requirement by including the environment in the parameter search space. This generalization of MFP involves defining an appropriate high‐resolution cost function, parametrizing the search space of the environment and source, constructing solutions of the wave equation, and utilizing a nonlinear optimization method to search the parameter landscape for the global minimum of the cost function. Focalization is implemented using cost functions based on ray theory and wave theory, empirical orthogonal functions for the environmental description, and simulated annealing for optimization. Numerical simulations are presented to demonstrate the feasibility of focalization.

Dynamic linear models with switching

Abstract: The problem of modeling change in a vector time series is studied using a dynamic linear model with measurement matrices that switch according to a time-varying independent random process. We derive filtered estimators for the usual state vectors and also for the state occupancy probabilities of the underlying nonstationary measurement process. A maximum likelihood estimation procedure is given that uses a pseudo-expectation-maximization algorithm in the initial stages and nonlinear optimization. We relate the models to those considered previously in the literature and give an application involving the tracking of multiple targets.

Exciting trajectories for the identification of base inertial parameters of robots

Abstract: A common way to identify the inertial parameters of robots is to use a linear model in relation to the parameters and standard least squares (LS) techniques. The authors present a method to generate exciting identification trajectories in order to minimize the effect of noise and error modeling on the LS solution. Using nonlinear optimization techniques, the condition number of a matrix W obtained from the energy model is minimized and the scaling of its terms is carried out. An example of a 3 degree of freedom robot is presented. >

Optimization approaches to nonlinear model predictive control

Abstract: With the development of sophisticated methods for nonlinear programming and powerful computer hardware, it now becomes useful and efficient to formulate and solve nonlinear process control problems through on-line optimization methods This paper explores and reviews control techniques based on repeated solution of nonlinear programming (NLP) problems Here several advantages present themselves These include minimization of readily quantifiable objectives, coordinated and accurate handling of process nonlinearities and interactions, and systematic ways of dealing with process constraints We motivate this NLP-based approach with small nonlinear examples and present a basic algorithm for optimization-based process control As can be seen this approach is a straightforward extension of popular model-predictive controllers (MPCs) that are used for linear systems The statement of the basic algorithm raises a number of questions regarding stability and robustness of the method, efficiency of the control calculations, incorporation of feedback into the controller and reliable ways of handling process constraints Each of these will be treated through analysis and/or modification of the basic algorithm To highlight and support this discussion, several examples are presented and key results are examined and further developed 74 refs, 11 figs

A B-differentiable equation-based, globally and locally quadratically convergent algorithm for nonlinear programs, complementarity and variational inequality problems

Abstract: This paper presents a globally convergent, locally quadratically convergent algorithm for solving general nonlinear programs, nonlinear complementarity and variational inequality problems. The algorithm is based on a unified formulation of these three mathematical programming problems as a certain system of B-differentiable equations, and is a modification of the damped Newton method described in Pang (1990) for solving such systems of nonsmooth equations. The algorithm resembles several existing methods for solving these classes of mathematical programs, but has some special features of its own; in particular, it possesses the combined advantage of fast quadratic rate of convergence of a basic Newton method and the desirable global convergence induced by one-dimensional Armijo line searches. In the context of a nonlinear program, the algorithm is of the sequential quadratic programming type with two distinct characteristics: (i) it makes no use of a penalty function; and (ii) it circumvents the Maratos effect. In the context of the variational inequality/complementarity problem, the algorithm provides a Newton-type descent method that is guaranteed globally convergent without requiring the F-differentiability assumption of the defining B-differentiable equations.

Sensitivity and stability analysis for nonlinear programming

Abstract: We give a brief overview of important results in several areas of sensitivity and stability analysis for nonlinear programming, focusing initially on “qualitative” characterizations (e.g., continuity, differentiability and convexity) of the optimal value function. Subsequent results concern “quantitative” measures, in particular optimal value and solution point parameter derivative calculations, algorithmic approximations, and bounds. Our treatment is far from exhaustive and concentrates on results that hold for smooth well-structured problems.

A screening and optimization approach for the retrofit of heat-exchanger networks

Abstract: On presente une methode systematique pour la readaptation de reseaux d'echangeurs de chaleur

A note on “level schedules for mixed-model assembly lines in just-in-time production systems”

Abstract: This note formulates an assignment problem for obtaining optimal level schedules for mixed-model assembly lines in JIT production systems. The problem was formulated as a quadratic integer programming problem in a recent paper by Miltenburg 1989 where, however, only enumerative algorithms and heuristics were proposed for its solution. Our assignment formulation can also be extended to more general objective functions than the one used by Miltenburg.

Optimal finite-thrust spacecraft trajectories using collocation and nonlinear programming

Abstract: A new method is described for the determination of optimal spacecraft trajectories in an inverse-square field using finite, fixed thrust. The method employs a recently developed direct optimization technique that uses a piecewise polynomial representation for the state and controls and collocation, thus converting the optimal control problem into a nonlinear programming problem, which is solved numerically. This technique has been modified to provide efficient handling of those portions of the trajectory that can be determined analytically, i.e., the coast arcs. Among the problems that have been solved using this method are optimal rendezvous and transfer (including multirevolution cases) and optimal multiburn orbit insertion from hyperbolic approach.

Algorithms for nonlinear bilevel mathematical programs

Abstract: The bilevel programming problem (BLPP) is a model of a leader-follower game in which play is sequential and cooperation is not permitted. Some basic properties of the general model are developed, and a conjecture relevant to solution procedures is presented. Two algorithms are presented for solving various versions of the game when certain convexity conditions hold. One algorithm relies upon a hybrid branch-and-bound scheme and does not guarantee global optimality. Another is based on objective function cuts and, barring numerical stability problems with the optimizer, is guaranteed to converge to an epsilon -optimal solution. The performance of the two algorithms is examined using randomly generated test problems. The computational performance of the branch-and-bound algorithm is explored, and the cutting-plane algorithm is used to determine whether or not the branch-and-bound algorithm is uncovering global optima. >

Methodology for Optimal Operation of Pumping Stations in Water Distribution Systems

Abstract: A methodology based on solving a large‐scale nonlinear programming problem is presented for the optimal operation of pumping stations in water distribution systems. Optimal operation refers to the scheduling of pump operation that results in the minimum operating cost for a given set of operating conditions. The mathematical model for pump operation is a large nonlinear programing problem. The methodology is based on an optimal control framework in which a nonlinear optimization model interfaces with a hydraulic simulation model, which is used to implicitly solve the conservation of flow and energy equations describing the hydraulics of flow in the optimization model. The methodology has been applied to a pressure zone of the Austin, Texas, water distribution system, showing how a reduction in operating costs could be accomplished using the simulation‐optimization model developed.

Robust capacity planning under uncertainty

Abstract: The existence of uncertainty influences the investment, production and pricing decision of firms. Therefore, capacity expansion models need to take into account uncertainty. This uncertainty, may arise because of errors in the specification, statistical estimation of relationships and in the assumptions of exogenous variables. One such example is demand uncertainty. In this paper, a cautious capacity planning approach is described for solving problems in which robustness to likely errors is needed. The aim is to cast the problem in a deterministic framework and thereby avoid the complexities inherent in nonlinear stochastic formulations. We adopt a robust approach and minimize an augmented objective function that penalises the sensitivity of the objective function to various types of uncertainty. The robust or sensitivity approach is compared with Friedenfelds' equivalent deterministic demand method. Using numerical results from a large nonlinear programming capacity planning model, it is shown that as ca...

Path planning for mobile manipulators for multiple task execution

Abstract: The planning problem for a mobile manipulator system that must perform a sequence of tasks defined by position, orientation, force, and moment vectors at the end-effector is considered. Each task can be performed in multiple configurations due to the redundancy introduced by mobility. The planning problem is formulated as an optimization problem in which the decision variables for mobility (base position) are separated from the manipulator joint angles in the cost function. The resulting numerical problem is nonlinear with nonconvex, unconnected feasible regions in the decision space. Simulated annealing is proposed as a general solution method for obtaining near-optimal results. The problem formulation and numerical solution by simulated annealing are illustrated for a manipulator system with three degrees of freedom mounted on a base with two degrees of freedom. The results are compared with results obtained by conventional nonlinear programming techniques customized for the particular example system. >

Geodesic convexity in nonlinear optimization

Abstract: The properties of geodesic convex functions defined on a connected RiemannianC2k-manifold are investigated in order to extend some results of convex optimization problems to nonlinear ones, whose feasible region is given by equalities and by inequalities and is a subset of a nonlinear space.

Parameter Estimation for Water Distribution Networks

Abstract: Calibration of a water distribution network is a long, tedious task, if analyzed by an engineer, with no guarantee of determining the proper system parameters. In addition, more utilities are moving toward automated control and wish to estimate the state of the network based upon telemetry data, A rigorous, nonlinear programming algorithm, which incorporates a network simulation model, is presented to solve these problems. The model is capable of analyzing one or more independent demand patterns, or extended period simulations, or both. The model assumes the measurements are exact and has an objective of minimizing the sum of the squares or absolute values of the differences between observed and estimated values of pipe flows and nodal pressure heads. The model consistently finds optimal solutions with the objective function equal to zero with exact data. However, the estimated parameters (pipe roughness coefficients, valve settings, and nodal demands) are not always the true values, which points to a nee...

Optimized feature extraction and the Bayes decision in feed-forward classifier networks

Abstract: The problem of multiclass pattern classification using adaptive layered networks is addressed. A special class of networks, i.e., feed-forward networks with a linear final layer, that perform generalized linear discriminant analysis is discussed, This class is sufficiently generic to encompass the behavior of arbitrary feed-forward nonlinear networks. Training the network consists of a least-square approach which combines a generalized inverse computation to solve for the final layer weights, together with a nonlinear optimization scheme to solve for parameters of the nonlinearities. A general analytic form for the feature extraction criterion is derived, and it is interpreted for specific forms of target coding and error weighting. An important aspect of the approach is to exhibit how a priori information regarding nonuniform class membership, uneven distribution between train and test sets, and misclassification costs may be exploited in a regularized manner in the training phase of networks. >

A Sequential Linearization Approach for Solving Mixed-Discrete Nonlinear Design Optimization Problems

Abstract: Design optimization models of often contain variables that must take only discrete values, such as standard sizes. Nonlinear optimization problems with a mixture of discrete and continuous variables are very difficult, and existing algorithms are either computationally intensive or applicable to models with special structure. A new approach for solving nonlinear mixed-discrete problems with no particular structure is presented here, motivated by its efficiency for models with extensive monotonicities of the problem’s objective and constraint functions with respect to the design variables. It involves solving a sequence of mixed-discrete linear approximations of the original nonlinear model. In this article, a review of previous approaches is followed by description of the resulting algorithm, its convergence properties and limitations. Several illustrative examples are given. A sequel article presents a detailed algorithmic implementation and extensive computational results.

Nonlinear predictive control of uncertain processes: Application to a CSTR

Abstract: Nonlinear predictive control (NLPC) is an effective strategy for controlling nonlinear chemical processes with constraints and time delays In this article, a number of important issues in NLPC are addressed, emphasizing continuous stirred tank reactors (CSTR's) with parametric and model structure uncertainty In particular, the effects of various choices for the initial conditions are discussed The selection of initial conditions is particularly important in the presence of plant/model mismatch A nonlinear programming-based approach is used for process identification The effect of model structure uncertainty is included in our analysis by using a cascade control structure on the coolant temperature Our CSTR results indicate that a simple PI (with anti-reset windup) cascade loop on coolant temperature is adequate for operation over a wide range of operating conditions A particularly interesting result of this work is that a predictive controller based on an open-loop observer can be used to stabilize an open-loop unstable process, although this is not recommended in practice