PRODUCTION AND ENERGY OPTIMIZATION IN AN INDUSTRIAL COMPLEX: A GENETIC ALGORITHM APPROACH A. Santos ⌡ ⌡⌡ , A. Dourado ⌡⌡ ⌡ Instituto Superior de Engenharia do Instituto Politécnico de Coimbra Quinta da Nora, apartado 4065, 3030 Coimbra, Portugal ⌡⌡ Departamento de Engenharia Informática da Universidade de Coimbra Pólo II, Pinhal de Marrocos, 3030 Coimbra, Portugal e-mail: amancio@isec.pt Keywords: Production Scheduling; Genetic Algorithms; Pulp and Paper ABSTRACT The pulp and paper industry exhibits nowadays an increasing need for efficient management of all those factors which may provide a reduction in operation costs. This leads to the necessity of an adequate optimization system which enables the generation of one or more optimum strategies with several objectives that fulfil the required restrictions. Herein we propose a system that looks for the optimum assignment for all the production sections in a particular mill of the kraft pulp and paper industry in order to optimize energy costs and the production rate changes using a genetic algorithm for the optimization task. This system is intended to fulfil all programmed or forced maintenance shutdowns as well as all the imposed reductions in production rates. INTRODUCTION A major number of continuos production industries can be described by a group of departments responsible for some specific operations and separated by intermediate buffers. The kraft pulp and paper is one of these industries. Consider the notation of fig. 1, suggested in [1], where buffer j, with level ( x j j m = 1, , K , receives the production from the department i, working at rate ( 29 u i n i = 1 , , K units, and delivers the raw material to department i+1, working at rate u i 1 units; b u ji i , + + ⋅ 1 1 units are consumed from buffer j for each unit of production u i + 1 . This work is based on the case study of the flowsheet of the Centro Fabril de Viana da Portucel, E. P., represented in fig. 5. u i x j u i + 1 Fig. 1. Flowsheet example with 2 departments and 1 buffer. Pulp mills are rather complex systems where shutdowns and disturbances propagate and influence very easily all the mill. This will lead to mass and energy losses due to chemical incorrect dosing and consequently to production losses and quality breakdowns. A production control system must then follow the mill’s actual state so that the production targets are achieved. During the last decade the optimization area has undergone a considerable growth in such a way that many of engineering problems can now be solved with the aid of non-deterministic methods. Genetic algorithms are included in the probabilistic methods’ family which, generally, are considered more robust than the random ones, since they incorporate their own search techniques. In this work a GA approach is used based on multicriteria constraint-handling techniques. Several methods exist for handling constraints by genetic algorithms in optimization problems. The technique used here [5] is based on preserving feasibility of solutions using specialized operators which are closed on the feasible part of the search space. These operators, namely crossover and mutation, transform feasible solutions into other feasible solutions. The basic idea behind this method lies in (i) the elimination of the equalities present in the constraint set and in the (ii) use of specific operators which guarantee that individuals are kept in the feasible space. In the next sections a set of considerations will be presented as for the production scheduling as for