Optimization of global and local pollution control in electricity production from coal burning

World electricity generation is expected to strongly increase over the next 25 years, mainly due to the demand of the emerging economies. In this context, coal is likely to remain as one of the most important energy sources. Several pollution control devices have been proposed so far as Best Available Techniques (BATs) to retrofit coal combustion plants in order to minimize their environmental impact. In this work we have developed a systematic method based on multi-objective optimization for the optimal design of pollution control devices in coal-fired plants that considers simultaneously economic and environmental criteria. Our formulation includes nonlinear models for predicting the cost and performance of the abatement technologies. The design task is formulated in mathematical terms as a bi-criteria mixed-integer nonlinear programming (MINLP) problem, the solution of which is defined by a set of Pareto points that represent the optimal trade-off between the two objective functions. The applicability of this method is explored through a case study based on a pulverized coal power plant of 500 MW (gross), burning low-sulfur bituminous coal. Numerical results show that it is possible to reduce the impact of these facilities at a marginal increase in cost by adjusting the operating conditions and capacities of the control pollution devices.

► We optimized the pollution control design in retrofitted coal-fired plants.
► Non linear models give reality to the performance results of control devices.
► The design task is formulated as a mixed-integer nonlinear programming problem.
► The reduction of local environmental impact requires less investment than global.
► Environmental indicators tend to behave in a non-conflictive manner in this problem.