Efficient Nonlinear Programming Algorithms for Chemical Process Control and Operations

Nonlinear programming (NLP) has been a key enabling tool for model-based decision-making in the chemical industry for over 50 years. Opti-mization is frequently applied in numerous ar-eas of chemical engineering including the de-velopment of process models from experimen-tal data, design of process flowsheets and equip-ment, planning and scheduling of chemical pro-cess operations, and the analysis of chemical pro-cesses under uncertainty and adverse conditions. These off-line tasks frequently require the solu-tion of NLPs formulated with detailed, lareg-scale process models. More recently, these tasks are complemented by time-critical, on-line optimization problem

Decomposition Strategy for Designing Flexible Chemical Plants.

One of the main computational problems faced in the optimal design of flexible chemical plants with multi-period operation is the large number of decision variables that are involved in the corresponding nonlinear programming formulation. To overcome this difficulty, a decomposition technique based on a projection- restriction strategy is suggested to exploit the block-diagonal structure in the constraints. Successful application of this strategy requires an efficient method to find an initial feasible point, and the extension of current equation ordering algorithms for adding systematically inequality constraints that become active. General trends in the performance of the proposed decomposition technique are presented through an example

Optimal Process Design Under Uncertainty.

A rigorous mathematical formulation is presented for the problem of optimal design under uncertainty. This formulation involves a nonlinear infinite programming problem in which an optimization is performed on the set of design and control variables, such that the inequality constraints of the chemical plant are satisfied for every parameter value that belongs to a specified polyhedral region. To circumvent the problem of infinite dimensionality in the constraints, an equivalence for the feasibility condition is established which leads to a max-min-max constraint. It is shown that if the inequalities are convex, only the vertices in the polyhedron need to be considered to satisfy this constraint. Based on this feature, an algorithm is proposed which uses only a small subset of the vertices in an iterative multiperiod design formulation. Examples are presented to illustrate the application to flexible design problems

Optimization Strategies for Flexible Chemical Processes.

The objective of this paper is to give an overview of the optimization strategies that are required when designing chemical processes in which the existence of regions of feasible steady- state operation must be ensured in the face of parameter variations. Two major areas are considered: optimal design with a fixed degree of flexibility, and design with optimal degree of flexibility. For the first area the problems of multiperiod design, and design under uncertainty are analyzed. For the second area the problem of deriving an index of flexibility in the context of multiobjective optimization is discussed. As shown in the paper, the major challenge in these problems lies in the development of efficient solution procedures for large scale nonlinear programs which are either highly structured, or otherwise involve an infinite number of constraints

Energy optimization in the process industries : unit commitment at systems level

In previous work, the unit commitment problem has been formulated as a non-convex MINLP, which is computationally expensive to solve. To circumvent this problem, we reformulate in this paper the problem as a convex MIQCP and derive the relevant constraints using propositional logic. We consider a specific problem based on a network of gas and oil fired power generators. Numerical results are presented using several methods (e.g. CPLEX for MIQCP, and DICOPT, SBB for MINLP). It is shown that proposed convex MIQCP reformulation can be solved much faster than previous non-convex models reported in the literature

Optimizing environmental and economic impacts in supply chains in the FMCG industry

In this paper, the environmental impacts of a supply chain of a Fast Moving Consumer Goods (FMCG) company are considered. The environmental impacts are evaluated using the Eco-indicator 99. In the optimization of the tactical planning decisions both the environmental impacts and the total costs are considered using the ε-constraint method for identifying a set of Pareto-optimal solutions. For a case study containing 10 Stock-Keeping Units (SKUs), which was optimized with a 1% optimality tolerance, the environmental impacts could be reduced by 2.9% without increasing the total costs. A further reduction of environmental impacts of up to 6.3% was possible at an increase in total costs of 5.2%. An SKU decomposition algorithm was applied to optimize a larger case study containing 100 SKUs