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The purpose of this study is to propose a control method that optimizes the set point of the variable air volume (VAV) terminal unit using real-time prediction model of indoor thermal and air environment and energy consumption. Indoor thermal environment and air environment are predicted through indoor load and carbon dioxide (CO2) concentration. A prediction model was developed through operation data of the VAV terminal unit by artificial neural network (ANN) algorithm. The developed prediction model was used for real-time set point control of the VAV terminal unit. The optimal control of VAV terminal unit can be expressed in 3 step. First, it predicts the current indoor load and CO2 concentration. Second, all supply temperatures and supply air flow rate that can be provided in the predicted condition are repeatedly simulated. Finally, the set point of the minimum energy consumption is applied to the control. The evaluation of the proposed control method was compared with the dual maximum control logic. Comparative evaluation was performed using TRNSYS. It was confirmed that the proposed control method reduced the reheat coil energy consumption by about 20% and the air supply fan energy consumption by about 17% compared to the existing control method.

Cinnamomum cassia (cassia) is a tropical aromatic evergreen tree of the Lauraceae family well known for its fragrance and spicy flavor and widely used in Asian traditional medicine. It has recently garnered attention for its diverse potential health benefits, including anti-inflammatory, anti-cancer, and anti-diabetic properties. However, the gastroprotective effect of C. cassia, particularly against ethanol-induced gastric damage, remains unclear. We investigated the potential gastroprotective property of C. cassia and the underlying mechanisms of action in a rat model of ethanol-induced gastric injury. To assess its effectiveness, rats were fed C. cassia for a 14-day period prior to inducing gastric damage by oral administration of ethanol. Our results indicated that pre-treatment with C. cassia mitigated ethanol-induced gastric mucosal lesions and bleeding. Reduced gastric acid secretion and expression of acid secretion-linked receptors were also observed. Additionally, pretreatment with C. cassia led to decreased levels of inflammatory factors, including TNF-α, p-p65, and IκBα. Notably, C. cassia upregulated the expressions of HO1 and HSP90, with particular emphasis on the enhanced expression of PAS and MUC, the crucial gastric mucosa defense molecules. These findings suggest that C. cassia has protective effects on the gastric mucosa and can effectively reduce oxidative stress and inflammation. [ABSTRACT FROM AUTHOR]

Ground heat accumulation caused by imbalanced heating and cooling loads in a building can cause the heat-source temperature to increase as the operating age of a geothermal heat pump (GHP) system increases. An alternative system to improve upon this situation is the hybrid GHP system. This study reviews existing research on GHP systems and hybrid GHP systems, variable water flow (VWF) control, and coefficient of performance (COP) prediction. Generally, constant flow control is applied to the circulating pump to provide a flow rate according to the maximum load. The need for VWF control was identified because the hybrid GHP system is used mainly as a heating and cooling heat source system for partial loads rather than the entire building load. Previous studies on predicting the COPs of GHP systems developed prediction models by selecting input values based on mathematical models, collecting data through multiple measurement points, and utilizing data from production environments. The model can be limited by the field environment, and it is necessary to predict the COP using machine learning based on existing field monitoring data. [ABSTRACT FROM AUTHOR]