Performance assessment and optimization of liquid desiccant dehumidifier system using intelligent models and integration with solar dryer

Abstract

Realistic performance predictions are necessary to control the liquid desiccant dehumidification systems effectively. In the present study, the application of artificial intelligence (AI) based artificial neural network (ANN), gene expression program (GEP) and adaptive neuro-fuzzy inference system (ANFIS) is investigated to estimate the dehumidifier effectiveness of desiccant dehumidifier systems while considering the effect of moisture removal rate and sensible heat factor as performance characteristics. It is found that the AI-based GEP model has the best prediction capability compared to the other developed AI models. In addition, the sensitivity analysis of independent parameters on the system performance parameters is estimated using the cosine amplitude method. The results demonstrate that the inlet desiccant temperature and specific humidity have a more substantial influence on the dehumidifier effectiveness and sensible heat ratio. Further, an algorithm involving the combination of multi-objective particle swarm optimization (MOPSO) with the GEP model is developed to optimize the input process parameters and to achieve better dehumidification performance. Lastly, based on obtained optimal operating conditions, a case study on a dehumidifier-integrated solar dryer is proposed for food/agriculture products drying applications using AI-based GEP-MOPSO model. It is also observed that the inlet water temperature and solar intensity have a significant impact on the useful heat gain of the solar collector.