There is a great incentive for developing systematic approaches that effectively identify strategies for planning oilfield complexes. This paper proposes an MILP that relies on a reformulation of the model developed by Tsarbopoulou (UCL M.S. Dissertation, London, 2000). Moreover, a bilevel decomposition technique is applied to the MILP. A master problem determines the assignment of platforms to wells and a planning subproblem calculates the timing for fixed assignments. Furthermore, a heuristic search procedure that relies on the distance between platforms and wells is applied in order to reduce the search region. Results show that the decomposition approach using heuristic generates optimal solutions for instances of up to 500 wells and 25 platforms in 10 discrete time periods that otherwise could not be solved with a full-scale approach. One important feature regarding these instances is that they correspond to problems of real-world dimension.

Pipelines provide an economic mode of fluid transportation for petroleum systems, specially when large amounts of these products have to be pumped for large distances. The system discussed in this paper is composed of a petroleum refinery, a multiproduct pipeline connected to several depots and the corresponding consumer markets that receive large amounts of gasoline, diesel, LPG and aviation fuel. An MILP optimization model that is based on a convex-hull formulation is proposed for the scheduling system. The model must satisfy all the operational constraints, such as mass balances, distribution constraints and product demands. Results generated include the inventory levels at all locations, the distribution of products between the depots and the best ordering of products in the pipeline.

This paper addresses the problem of integrating in a single model operational optimization and cyclic scheduling of continuous plants. Considered are multiproduct, multistage plants with finite intermediate storage capacity (FIS). A combined optimization approach introduces synergic effects for more effective scheduling and plant operation (Alle and Pinto, 2001a,b). The representation proposed for this problem results in an MINLP model with a nonconvex feasible region and nonconvex objective function. In order to deal with nonconvexity, a spatial branch-and-bound global optimization algorithm is applied. Results show that the global approach is effectively able to yield more profitable solutions than those obtained by local optimization methods.

An optimization method for determining the best configuration(s) of gasketed plate heat exchangers is presented. The objective is to select the configuration(s) with the minimum heat transfer area that still satisfies constraints on the number of channels, the pressure drop of both fluids, the channel flow velocities and the exchanger thermal effectiveness. The configuration of the exchanger is defined by six parameters, which are as follows: the number of channels, the numbers of passes on each side, the fluid locations, the feed positions and the type of flow in the channels. The resulting configuration optimization problem is formulated as the minimization of the exchanger heat transfer area and a screening procedure is proposed for its solution. In this procedure, subsets of constraints are successively applied to eliminate infeasible and nonoptimal solutions. Examples show that the optimization method is able to successfully determine a set of optimal configurations with a minimum number of exchanger evaluations. Approximately 5 % of the pressure drop and channel velocity calculations and 1 % of the thermal simulations are required for the solution.

It is known that the optimal control may introduce significant economical benefits into production processes, thus being an important and challenging research area with practical relevance. The modeling and optimization of biotechnological processes has been object of research and their related results have generated improvements in operating conditions and strategies, however, the inherent features of dynamical bioprocesses prevent the application of conventional optimization algorithms, hence making necessary the development of tailored methods and strategies. The objective of this work is to develop mathematical programming strategies for simultaneous optimization of dynamic systems and evaluate their computational performance. Simultaneous optimization with orthogonal collocation is applied to a simplified model for biosynthesis of penicillin from glucose, which was studied by Cuthrell and Biegler (1989). The results show that discretization of differential equations systems (DAE) by orthogonal collocation in finite elements efficiently transforms dynamic optimization problems into nonlinear programming (NLP) problems, enabling to solve complex problems with several control variables and minimizing the approximation error.

The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP) optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of Moro et al. (1998) is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG) area for maximization of the production of petrochemical-grade propane and product delivery.

The problems of scheduling and optimization of operational conditions in multistage, multiproduct continuous plants with intermediate storage are simultaneously addressed. An MINLP model, called TSPFLOW, which is based on the TSP formulation for product sequencing, is proposed to schedule the operation of such plants. TSPFLOW yields a one-order-of-magnitude CPU time reduction as well as the solution of instances larger than those formerly reported (Pinto and Grossmann, 1994). Secondly, processing rates and yields are introduced as additional optimization variables in order to state the simultaneous problem of scheduling with operational optimization. Results show that trade-offs are very complex and that the development of a straightforward (rule of thumb) method to optimally schedule the operation is less effective than the proposed approach.

In this work, an optimization model that considers thermal and hydraulic interactions is developed for a cooling water system. It is a closed loop consisting of a cooling tower unit, circulation pump, blower and heat exchanger-pipe network. Aside from process disturbances, climatic fluctuations are considered. Model constraints include relations concerning tower performance, air flowrate requirement, make-up flowrate, circulating pump performance, heat load in each cooler, pressure drop constraints and climatic conditions. The objective function is operating cost minimization. Optimization variables are air flowrate, forced water withdrawal upstream the tower, and valve adjustment in each branch. It is found that the most significant operating cost is related to electricity. However, for cooled water temperatures lower than a specific target, there must be a forced withdrawal of circulating water and further makeup to enhance the cooling tower capacity. Additionally, the system is optimized along the months. The results corroborate the fact that the most important variable on cooling tower performance is not the air temperature itself, but its humidity.

The main objective of this paper is to develop a nonlinear planning model for refinery production. The model described here represents a general petroleum refinery and its framework allows the implementation of nonlinear process models as well as blending relations. This model assumes the existence of several processing units, producing a variety of intermediate streams, with different properties, that can be blended to constitute the desired kinds of products. Two real-world applications are developed, one for the planning of diesel production in the RPBC refinery in Cubatão (SP) and the other for the general production planning in the REVAP refinery in S. José dos Campos (SP). In both cases, different market scenarios were analyzed using the planning optimization and the results were compared with the current situation, in which there is no extensive use of planning decision support tools. Results revealed that there is a very large potential of profitability embedded in the planning activity, reaching several millions of dollars per year.

Most industrial processes generate liquid waste, which requires treatment prior to disposal. These processes are divided into sectors that generate effluents with time dependent characteristics. Each sector sends the effluent to wastewater treatment plants through pumping-stations. In general, activated sludge is the most suitable treatment and consists of equalization, aeration and settling tanks. During the treatment, there is an increase in the mass of microorganisms, which needs to be removed. Sludge removal represents the major operating costs for wastewater treatment plants. The objective of this work is to propose an optimization model to minimize sludge generation using a superstructure in which the streams from pumping-stations can be sent to the equalization tank. In addition, the aeration tank is divided into cells that can be fed in series and parallel. The model relies on mass balances, kinetic equations, and the resulting Nonlinear Programming problem generates the best operational strategy for the system feed streams with a high substrate removal. Reductions of up to 30 % can be achieved with the proposed strategy maintened BOD efficiency removal upper than 98 %.