Estratégia baseada em horizonte rolante para otimização da distribuição de gases siderúrgicos com incertezas
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Data
2019-07-17
Autores
Pena, Joao Gustavo Coelho
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Universidade Federal do Espírito Santo
Resumo
Manufacturing iron-and steel is one of the most energy intensive and pollutant in dustrial activities. On the other hand, the majority of these pollutant gases can be used as fuel for cogeneration of electricity and process steam; in that case, then the efficient utilisation of these gases is significant for energy saving and reduction. However, the management of this system is a complex activity, mainly because of the imbalances between the production and consumption profiles of the gases, the capacity limitations of the gas accumulators and their operational restrictions, as well as the restrictions for the use of energy in the thermoelectric power plants. As a result, when a temporary excess of byproduct gases occurs over a timescale, the byproduct gasholder exceeds capacity, and this leads to byproduct gas flaring, which indicates an economic loss and pollution of the environment. However, a shortage of byproduct gas causes mechanical trouble to the byproduct gasholder and affects the production process. Thus, it is of great impor tance to optimise the scheduling and distribution of byproduct gases to reduce byproduct gas flaring or shortage, and to maintain the stability of the byproduct gases distribution system. This thesis addresses the real-time by-product gas scheduling in an integrated iron and steel-making industry with uncertainty in by-product gas flows by means of a rol ling horizon algorithm. Adaptive time-series models determined from real data perform forecast for each producer and consumer of by-product gases in main units of the steel making plant. The individual consumptions of the blast furnace and coke oven gases are modelled using the seasonal Holt-Winters method with smoothing constants estimated via genetic algorithm, whereas the individual productions of the blast furnace and coke oven are identified from autoregressive and integrated moving-average. LDG gas production is forecasted using a heuristic method that leverages the operational information. The model’s parameters are updated periodically due to the nonlinearities present in the time series. After the forecasting phase, the algorithm performs short-term decisions using a MILP optimization model, that minimizes the imbalance between the random dynamics of the by-product fuel generation and consumption and maximizes the energy efficiency. Computational simulations suggest that the operational stability of the gas holders and the electrical energy production increase, whereas the waste of gases in flare stack decreases, when the control horizon of the rolling horizon algorithm is reduced. Particle swarm opti mization was applied to identify reasonable penalty factors which were used in the MILP model to obtain reasonable optimisation of the byproduct gas system.
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Algoritmo de horizonte rolante, , Controle preditivo , Otimização matemática , Gasômetro , Siderurgia , Análise de séries temporais , Energia elétrica e calor , Cogeração , By-product gas forecasting , By-product gas distribution , Energy saving , Gas holder level control , Mixed Integer Linear Programming , Particle Swarm Optimization, Rolling horizon algorithm , Scheduling, Seasonal exponential smoothing method , ARIMA