Juan M. Salazar

Minimization of Water Consumption under Uncertainty for a Pulverized Coal Power Plant

Coal-fired power plants are large water consumers. Water consumption in thermoelectric generation is strongly associated with evaporation losses and makeup streams on cooling and contaminant removal systems. Thus, minimization of water consumption requires optimal operating conditions and parameters, while fulfilling the environmental constraints. Several uncertainties affect the operation of the plants, and this work studies those associated with weather. Air conditions (temperature and humidity) were included as uncertain factors for pulverized coal (PC) power plants. Optimization under uncertainty for these large-scale complex processes with black-box models cannot be solved with conventional stochastic programming algorithms because of the large computational expense. Employment of the novel better optimization of nonlinear uncertain systems (BONUS) algorithm, dramatically decreased the computational requirements of the stochastic optimization. Operating conditions including reactor temperatures and pressures; reactant ratios and conditions; and steam flow rates and conditions were calculated to obtain the minimum water consumption under the above-mentioned uncertainties. Reductions of up to 6.3% in water consumption were obtained for the fall season when process variables were set to optimal values. Additionally, the proposed methodology allowed the analysis of other performance parameters like gas emissions and cycle efficiency which were also improved.

Rigorous-simulation pinch-technology refined approach for process synthesis of the water-gas shift reaction system in an IGCC process with carbon capture

Integrated gasification combined cycle (IGCC) technology is becoming increasingly more competitive among advanced power generation systems suitable for carbon capture. As an emerging technology, many different IGCC process configurations have been heuristically proposed to meet even more aggressive economic and environmental goals. One attractive design combines gasification with a water–gas shift (WGS) reaction system, pressure swing adsorption, and chemical-looping combustion (CLC) for CO2 removal prior to feeding the fuel gas to the combined cycle for power production. The WGS reaction step is required to convert CO to CO2 and the extent of conversion is determined by the degree of carbon capture required in the CLC step. As a first towards optimizing the overall energy efficiency of this IGCC process, we apply heat exchanger network synthesis (HENS) to the WGS reaction system. This particular part of the process was chosen because of its evident integration potential (steam required for the WGS reactions can be generated by recovering energy released by the same reactions) and the influence of some of the gasifier parameters (temperature and pressure) on its performance and on all the subsequent parts of the process. After generating alternative designs using Aspen Energy Analyzer (AEA), the HENS problem was formulated in the sequential-modular Aspen Plus simulator using a process superstructure approach and solved by mixed integer nonlinear programming (MINLP) algorithms. The HENS capability is implemented as CAPE-OPEN (CO) compliant unit operation and makes use of MINLP algorithms, namely Generalized Benders Decomposition (GBD), Outer Approximation (OA), Equality Relaxation (ER), Augmented Penalty (AP), and Simulated Annealing (SA). This MINLP-based HENS was used in the CO-compliant Aspen Plus simulator to obtain a design for the WGS reaction system that provided a cost of energy for the IGCC system with CO2 capture that was 28% lower than the base case.