This paper introduces a new optimization model for the single and multiple-effect evaporation (SEE/MEE) systems design, including vapor recompression cycle and thermal integration. The SEE/MEE model is specially developed for shale gas flowback water desalination. A superstructure is proposed to solve the problem, comprising several evaporation effects coupled with intermediate flashing tanks that are used to enhance thermal integration by recovering condensate vapor. Multistage equipment with intercooling is used to compress the vapor formed by flashing and evaporation. The compression cycle is driven by electricity to operate on the vapor originating from the SEE/MEE system, providing all the energy needed in the process. The mathematical model is formulated as a nonlinear programming (NLP) problem optimized under GAMS software by minimizing the total annualized cost. The SEE/MEE system application for zero liquid discharge (ZLD) is investigated by allowing brine salinity discharge near to salt saturation conditions. Additionally, sensitivity analysis is carried out to evaluate the optimal process configuration and performance under distinct feed water salinity conditions. The results highlight the potential of the proposed model to cost-effectively optimize SEE/MEE systems by producing fresh water and reducing brine discharges and associated environmental impacts.This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 640979. The financial support provided by the National Council for Scientific and Technological Development of Brazil (CNPq), under process No. 233953/2014-0 is also gratefully acknowledged

The temperature of operation is a key parameter in determining the performance and durability of a polymer electrolyte fuel cell (PEFC). Controlling temperature and understanding its distribution and dynamic response is vital for effective operation and design of better systems. The sensitivity to temperature means that uncertainty in this parameter leads to variable response and can mask other factors affecting performance. It is important to be able to determine the impact of temperature uncertainly and quantify how much PEFC operation is influenced under different operating conditions. Here, a simple lumped mathematical model is used to describe PEFC performance under temperature uncertainty. An analytical approach gives a measure of the sensitivity of performance to temperature at different nominal operating temperatures and electrical loadings. Whereas a statistical approach, using Monte Carlo stochastic sampling, provides a ‘probability map’ of PEFC polarisation behaviour. As such, a polarisation ‘area’ or ‘band’ is considered as opposed to a polarisation ‘curve’. Results show that temperature variation has the greatest effect at higher currents and lower nominal operating temperatures. Thermal imaging of a commercial air-cooled stack is included to illustrate the temporal and spatial temperature variation experienced in real systems