Fast prediction for multi-parameters(concentration,temperature and humidity)of indoor environment towards the online control of HVAC system

Heating,ventilation and air conditioning(HVAC)systems are the most energy-consuming building implements for the improvement of indoor environmental quality(IEQ).We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation,through the fast prediction methods by using low-dimensional linear ventilation model(LLVM)based artificial neural network(ANN)and low-dimensional linear temperature model(LLTM)based contribution ratio of indoor climate(CRI_((T))).To be continued for integrated control of multi-parameters,we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model(LLHM)and contribution ratio of indoor humidity(CRI_((H))),and thermal sensation index(TS)for assessment.CFD was used to construct the prediction database for CO_(2),temperature and humidity.Low-dimensional linear models(LLM),including LLVM,LLTM and LLHM,were adopted to expand database for the sake of data storage reduction.Then,coupling with ANN,CRI_((T)) and CRI_((H)), the distributions of indoor CO_(2) concentration,temperature,and humidity were rapidly predicted on the basis of LLVM-based ANN,LLTM-based CRIm and LLHM-based CRM respectively.Finally,according to the self-defined indices(i.e.,E_(V),E_(T),E_(H)),the optimal balancing between IEQ(indicated by CO_(2) concentration,PMV and TS)and energy consumption(indicated by ventilation rate,supplied air temperature and humidity)were synthetically evaluated.The total HVAC energy consumption could be reduced by 35%on the strength of current control strategies.This work can further contribute to development of the intelligent online control for HVAC systems.

Prediction of CO_(2) solubility in deep eutectic solvents using random forest model based on COSMO-RS-derived descriptors

This work presents the development of molecular-based mathematical model for the prediction of CO_(2) solubility in deep eutectic solvents(DESs).First,a comprehensive database containing 1011 CO_(2) solubility data in various DESs at different temperatures and pressures is established,and the COSMO-RS-derived descriptors of involved hydrogen bond acceptors and hydrogen bond donors of DESs are calculated.Afterwards,the efficiency of the input variables,i.e.,temperature,pressure,COSMO-RS-derived descriptors of HBA and HBD as well as their molar ratio,is explored by a qualitative analysis of CO_(2) solubility in DESs using a simple multiple linear regression model.A machine learning method namely random forest is then employed to develop more accurate nonlinear quantitative structure-property relationship(QSPR)model.Combining the QSPR validation and comparisons with literature-reported models(i.e.,COSMO-RS model,traditional thermodynamic models and equations of state methods),the developed QSPR model with COSMO-RS-derived parameters as molecular descriptors is suggested to be able to give reliable predictions of CO_(2) solubility in DESs and could be used as a useful tool in selecting DESs for CO_(2) capture processes.