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Phenol resins (PRs) are considered as relatively inexpensive adsorbents synthesized from agricultural biomass via employing a variety of synthesized procedures. The performance of PR for heat transformation application is not widely investigated. In this regard, the present study aims to evaluate the four PR derivative/refrigerant pairs, namely (i) KOH6-PR/CO2, (ii) SAC-2/HFC, (iii) KOH4-PR/ethanol, and (iv) KOH6-PR/ethanol, for adsorption cooling and adsorption heating applications. Ideal cycle analyses and/or thermodynamic modelling approaches were utilized comprising governing heat and mass balance equations and adsorption equilibrium models. The performance of the AHP system is explored by means of specific cooling energy (SCE), specific heating energy (SHE), and coefficient of performance (COP), both for cooling and heating applications, respectively. It has been realized that KOH6-PR/ethanol could produce a maximum SCE of 1080 kJ/kg/cycle and SHE of 2141 kJ/kg/cycle at a regeneration temperature (Treg) and condenser temperature (Tcond) of 80 °C, and 10 °C, respectively, followed by KOH4-PR/ethanol, SAC-2/HFC-32, and KOH6-PR/CO2. The maximum COP values were estimated to be 1.78 for heating and 0.80 for cooling applications, respectively, at Treg = 80 °C and Tcond = 10 °C. In addition, the study reveals that, corresponding to increase/decrease in condenser/evaporator pressure, both SCE and SHE decrease/increase, respectively; however, this varies in magnitude due to adsorption equilibrium of the studied PR derivative/refrigerant pairs.

Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions are critical for controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) and lighting systems to optimize plant growth and reduce financial losses. In this study, several machine models were employed to predict indoor air temperature in an even-span Mediterranean greenhouse. Radial Basis Function (RBF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) were applied using external parameters such as outside air, relative humidity, wind speed, and solar radiation. The results showed that an RBF model with the LM learning algorithm outperformed the SVM and GPR models. The RBF model had high accuracy and reliability with an RMSE of 0.82 °C, MAPE of 1.21%, TSSE of 474.07 °C, and EF of 1.00. Accurate temperature prediction can help farmers manage their crops and resources efficiently and reduce energy inefficiencies and lower yields. The integration of the RBF model into greenhouse control systems can lead to significant energy savings and cost reductions. [ABSTRACT FROM AUTHOR]

Solar radiation data are essential information for designing and studying various engineering systems such as building thermal performance, photovoltaics, solar thermal systems, and passive solar design. Several empirical models have been developed to simulate the available solar radiation on the earth's surface. This article presents a comprehensive review of cloud cover-based solar radiation models (CSRMs) for estimating the hourly global solar radiation (GSR). Many of these models have been developed according to the Angstrom–Prescott relationship for solar radiation and are mainly used to estimate the monthly average daily total solar radiation. Based on the comprehensive review of CSRMs, it could be stated that the Kasten-Czeplak model and the Lam-Li model are comparatively straightforward and precise for estimating the hourly GSR. Furthermore, the study evaluated the selected model (Lam-Li model) to estimate the hourly GSR on the horizontal surfaces for four different cities in Canada. Results revealed that the original Lam-Li model performs within an acceptable range based on the statistical indices (R2 = 0.77–0.80 and rRMSE = 39.7–44.0%); however, the performance of the newly modified model (M2) from this study performs significantly better (R2 = 0.8–0.82 and rRMSE = 3.65–39.5%) than the original model (M1). [ABSTRACT FROM AUTHOR]

Cloud cover based solar radiation models are relatively simple and convenient as the models require the input of cloud cover data which are mostly available from the meteorological stations. In this study, the performance of a cloud cover based solar radiation model (Kasten-Czeplak model) with original or locally fitted coefficients was evaluated for estimating the hourly global solar radiation for four different locations in Western Canada. The average value of R2, mean bias error, and root mean square error are 0.69, −61.6, and 157.9 W m−2, respectively, for the model with original coefficients, whereas 0.82, 4.4, 107.1 W m−2 with locally fitted coefficients. Results show that the Kasten-Czeplak model with locally fitted coefficients satisfactorily estimated the hourly solar radiation of four different locations in Western Canada. Also, the results indicate that the model with original coefficients has very limited accuracy under intermediate cloud cover conditions. [ABSTRACT FROM AUTHOR]