Multi-objective optimization of co2 emissions from refinery operations

Abstract

Oil refineries are under increasing pressure to reduce CO2 emissions to assist global reductions in greenhouse gases under international treaties such as the Kyoto protocol. Moreover, the growing demand for oil products and the continuing push for cleaner distillates lead to substantial increases in energy consumption and therefore as a consequence higher CO2 emissions from these same refineries. Therefore, the ability to estimate accurately CO2 emissions (emissions targeting) has become very prominent in modern refineries. To meet these challenges and survive in today’s very competitive fuel market, continuous optimization is imperative for refineries to raise their operations to new levels of performance. However, environmental and economic objectives often compete with one another and, thus, the best solution involves a trade-off between the different objectives. Multi-Objective Optimization (MOO) is a proven powerful optimization method to achieve a satisfactory trade-off between such competing or conflicting objectives. This thesis uses MOO for the first time to target CO2 emissions from refinery processes. For this purpose, a new MOO framework has been introduced to set a target for CO2 emissions from refinery processes and produce the trade-off solutions for the environmental and economic objectives of the refinery. The MOO framework includes rigorous mass and energy balance simulation, and process integration, and the genetic algorithm optimizer provides a comprehensive optimization approach. Rigorous models of the two most energy intensive oil refinery units, i.e. Crude Distillation Unit (CDU) and the Fluidized-bed Catalytic Cracker (FCC) and associated heat recovery system are analysed. The optimization approach is illustrated in different optimization problems of the refinery. The impacts of the crude type on the trade-off between the economic and environmental performances of the refinery units are studied and the optimum blend of different crudes is investigated. The crude type shows significant impacts on both the economic and environmental performance of the refinery units and a considerable amount of CO2 emissions can be avoided by changing the type of crude processed at minimum impact on the economic objectives. Different schemes of energy integration, namely, direct integration and total site, and their potential impacts on products revenue and CO2 emissions of the refinery are also studied. The trade-off between environmental and economic targets are also investigated in this thesis using a new graphical approach which can be used for targeting CO2 emissions associated with utility systems and energy resource networks. The optimization approach is used to target and allocate different energy resources to meet a specific energy demand and achieve both economic and environmental goals. This approach based on the principles of marginal energy cost and marginal CO2 emissions involves two new plots: the cost composite and CO2 emissions composite. These plots are used with the energy load shifting factor to shift between different alternative resources with minimum impacts on CO2 emissions and associated cost. The validity of the new graphical approach is demonstrated in two different cases of a single utility system for an industrial complex and for energy sector planning on a national basis. The principles of marginal energy cost and marginal CO2 emissions are also extended for predicting the optimal results of MOO, and therefore they provide a useful method for explaining the results from a purely stochastic algorithm. The results show that the accuracy of predicting the Pareto-optimal front using this method was excellent.