Modelling, Simulation and Optimisation of Multistage Humidification and Dehumidification Desalination Plant Using Solar Energy. Performance Evaluation and Improvement of the Humidification-Dehumidification Desalination Process through Modelling, Simulation and Optimisation Techniques

Serious social and economic disruptions are unfolding worldwide over the finite water and energy resources; hence, securing fresh water supply and employing renewable energy sources will help avoid catastrophic conflicts, continue modern lifestyles, and circumvent global warming and pollution. For this reason, a new method known as Humidification-Dehumidification (HDH) desalination process has been developed to address the challenge of water shortage. The aim of this research was to build the detailed mechanistic models with the increased capability to predict more accurately as well as to simulate and optimise the Multistage Humidification-Dehumidification (MHDH) desalination plant which is powered by solar energy. The poor prediction accuracy is major bottleneck for most of conventional models. Mechanistic models for HDH desalination process derived from non-linear mathematical equations offers a promising solution to overcome this challenge. This study proposes a mechanistic model which is formulated by combining the enthalpy equations with the models which govern the mass and heat transfer across a thin film that separates water and air phases within the humidifier and dehumidifier. The proposed model is validated by using the data which were obtained from the physical experiments. Moreover, an experimental rig was designed and fabricated to specifically generate the physical data. From the experimental and mathematical analysis, it was observed that the Recovery Ratio (RR) attained was increasing as temperature of the feed water increased. The RR was also increasing with the increase of dehumidifier’s surface area while it decreased with an increase of the packing size. Moreover, through a sensitivity analysis the highly influential parameters to the process model were identified to better understand the energy-efficient design principles and operating strategies for the maximum performance of the system. Finally, a two stages HDH hybrid system that uses solar and biomass as source of energy is proposed whereby, an optimisation problem is solved to achieve the optimum RR. A maximum of 2 stages were required for a system to operate optimally.Commonwealth Scholarship Commission in the UK (CSC) under PhD Scholarships Plan for Low and Middle Income Countrie

A process simulator interface for multiobjective optimization of chemical processes

© 2017 The (bio)chemical process industry is under an increasing pressure due to smaller margins and increasing societal and legislative demands for a sustainable future. In this context model-based optimization contributes to the solution because it serves to improve the processes’ performance. Furthermore, multiobjective optimization techniques provide the decision maker with a deeper insight in the tradeoffs when choosing an operating condition. However, an accurate process model is needed to apply these techniques efficiently. In this paper, a novel interface is developed between state-of-the-art gradient-based optimization techniques and the widely used process simulator Aspen Plus. Furthermore, specific challenges and solutions for overcoming the gap between process simulators and optimization tools are highlighted. The resulting interface allows gradient-based techniques to be exploited for optimization of complex industrial processes modeled in the advanced Aspen Plus environment. The interface ensures constraints satisfaction, and a higher computational performance than gradient free methods.status: publishe