Optimal design of multi-effect evaporation processes considering variable fresh water demands

PIETRASANTA, ARIANA; MUSSATI, MIGUEL C.; AGUIRRE, PÍO A.; BLANCO MARIGORTA, ANA MARÍA; MUSSATI, SERGIO F.

Las Palmas de Gran Canaria, Las Palmas

Congreso; Desalination for the Environment: Clean Water and Energy; 2020

European Desalination Society

This work addresses the optimal design of multi-effect evaporation systems (MEE) to obtain fresh water from seawater desalination subject to variable freshwater demand along the year. Four seasons with different freshwater demands are considered per year. The optimization task consists in finding a MEE unit that simultaneously satisfy each one of the fresh water demands at minimum total annual cost (TAC). To this end, a multi-period optimization mathematical model is developed in GAMS (general algebraic modeling system) platform and solved with CONOPT which employs a generalized reduced gradient method. The model includes the mass and energy balances, design equations and the corresponding cost model. The optimization problem consists in obtaining the size of the MEE unit (that is, the heat transfer area in each evaporation effect and pre-heater) and how each evaporation effect should be operated to minimize the TAC. It should be highlighted that the heat transfer areas of effects and pre-heaters, which are considered as optimization variables, should be the same for all of the seasons but they may be operated at different conditions (flow-rate, pressure, temperature and concentration). Also, the possibility of different stream`s flow-patterns are considered by the developed model. For instance, distillate extraction in each effect is a possibility that it is proposed as a decision model. The objective function is the minimization of the TAC which is calculated in terms of the annualized capital (CAPEX) and operating (OPEX) expenditures. CAPEX takes into account the costs associated to the heat transfer areas, civil work and seawater intake and pre-treatment while the OPEX includes the pre-treatment, maintenance and labor and steam costs.After the optimization, a sensitivity analysis is performed in order to investigate how the optimal solution is influenced by the main model parameters (number of effects, seawater salinity and temperature).