Irreversible faradic reactions in reverse electrodialysis (RED) are an emerging concern for scale-up, reducing the overall performance of RED and producing environmentally harmful chemical species. Capacitive RED (CRED) has the potential to generate electricity without the necessity of irreversible faradic reactions. However, there is a critical knowledge gap in the fundamental understanding of the effects of operational stack voltages of CRED on irreversible faradic reactions and the performance of CRED. This study aims to develop an active control strategy to avoid irreversible faradic reactions and pH change in CRED, focusing on the effects of a stack voltage (0.9-5.0 V) on irreversible faradic reactions and power generation. Results show that increasing the initial output voltage of CRED by increasing a stack voltage has an insignificant impact on irreversible faradic reactions, regardless of the stack voltage applied, but a cutoff output voltage of CRED is mainly responsible for controlling irreversible faradic reactions. The CRED system with eliminating irreversible faradic reactions achieved a maximum power density (1.6 W m-2) from synthetic seawater (0.513 M NaCl) and freshwater (0.004 M NaCl). This work suggests that the control of irreversible faradic reactions in CRED can provide stable power generation using salinity gradients in large-scale operations.

Manually laying out the floor plan for buildings with highly-dense adjacency constraints at the early design stage is a labour-intensive problem. In recent decades, computer-based conventional search algorithms and evolutionary methods have been successfully developed to automatically generate various types of floor plans. However, there is relatively limited work focusing on problems with highly-dense adjacency constraints common in large scale floor plans such as hospitals and schools. This paper proposes an algorithm to generate the early-stage design of floor plans with highly-dense adjacency and non-adjacency constraints using reinforcement learning based on off-policy Monte-Carlo Tree Search. The results show the advantages of the proposed algorithm for the targeted problem of highly-dense adjacency constrained floor plan generation, which is more time-efficient, more lightweight to implement, and having a larger capacity than other approaches such as Evolution strategy and traditional on-policy search.