Application of Neural Networks and multiple regression models in greenhouse climate estimation

Artificial Neural Networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. After a comprehensive literature survey on the application of ANNs in greenhouses, this work describes the results of using ANNs to predict the roof temperature, inside air humidity, soil temperature and inside soil humidity (Tri, RHia, Tis, RHis), in a semi-solar greenhouse according to use some inside and outside parameters in the institute of renewable energy in East Azerbaijan province, Iran. For this purpose, a semi-solar greenhouse was designed and constructed for the first time in Iran. The model database selected beside on the main and important factors influence the four above variables inside the greenhouse. Neural estimation models were constructed with (Vo, Tia, Toa, Ir, Tis, RHia, Tri) as the inputs and (Tri, RHis, Tis, RHia) as the outputs. Optimal parameters for the network were selected via a trial and error procedure on the available data. Results showed that MLP (Multilayer Perceptron) algorithm with 4-6-1(4 inputs in first layer, 6 neurons in hidden layer and an output) and 4-9-1(4 inputs in first layer, 9 neurons in hidden layer and an output) topologies can predict inside soil and air humidity and inside roof and soil temperature with a low error (RMSE=0.25°C, 0.30%, 1.06°C and 0.25% for Tri, RHis, Tis and RHia), respectively. Also the results showed that regression model has a low error to predict Tri (RMSE=0.71°C) and high error to estimate Tis (2.71°C), respectively. In overall, the error for regression model to predict all 4 parameters (Tri, RHis, Tis, RHia) was about 2 times higher than MLP method. It is concluded that ANN represents a promising tool for predicting inside climate in a greenhouse and will be useful in automatic greenhouses. For practical application, however, the farmers should use metrological and experimental data for 12 months of the year to decrease the prediction error

Modeling the CO2 separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks

Abstract Membranes are a potential technology to reduce energy consumption as well as environmental challenges considering the separation processes. A new class of this technology, namely mixed matrix membrane (MMM) can be fabricated by dispersing solid substances in a polymeric medium. In this way, the poly(4-methyl-1-pentene)-based MMMs have attracted great attention to capturing carbon dioxide (CO2), which is an environmental pollutant with a greenhouse effect. The CO2 permeability in different MMMs constituted of poly(4-methyl-1-pentene) (PMP) and nanoparticles was comprehensively analyzed from the experimental point of view. In addition, a straightforward mathematical model is necessary to compute the CO2 permeability before constructing the related PMP-based separation process. Hence, the current study employs multilayer perceptron artificial neural networks (MLP-ANN) to relate the CO2 permeability in PMP/nanoparticle MMMs to the membrane composition (additive type and dose) and pressure. Accordingly, the effect of these independent variables on CO2 permeability in PMP-based membranes is explored using multiple linear regression analysis. It was figured out that the CO2 permeability has a direct relationship with all independent variables, while the nanoparticle dose is the strongest one. The MLP-ANN structural features have efficiently demonstrated an appealing potential to achieve the highest accurate prediction for CO2 permeability. A two-layer MLP-ANN with the 3-8-1 topology trained by the Bayesian regulation algorithm is identified as the best model for the considered problem. This model simulates 112 experimentally measured CO2 permeability in PMP/ZnO, PMP/Al2O3, PMP/TiO2, and PMP/TiO2-NT with an excellent absolute average relative deviation (AARD) of lower than 5.5%, mean absolute error (MAE) of 6.87 and correlation coefficient (R) of higher than 0.99470. It was found that the mixed matrix membrane constituted of PMP and TiO2-NT (functionalized nanotube with titanium dioxide) is the best medium for CO2 separation

Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse

Greenhouses are one of the most effective cultivation methods with a yield per cultivated area up to 10 times more than free land cultivation but the use of fossil fuels in this production field is very high. The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it. There are many control methods, such as adaptive, feedback and intelligent control and they require a precise model. Therefore, many modeling methods have been proposed for this purpose; including physical, transfer function and black-box modeling. The objective of this paper is to modeling and experimental validation of some inside environment variables in an innovative greenhouse structure (semi-solar greenhouse). For this propose, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (38°10′N and 46°18′E with elevation of 1364 m above the sea level). The main inside environment factors include inside air temperature (Ta) and inside soil temperature (Ts) were collected as the experimental data samples. The dynamic heat transfer model used to estimate the temperature in two different points of semi-solar greenhouse with initial values. The results showed that dynamic model can predict the inside temperatures in two different points (Ta and Ts) with RMSE, MAPE and EF about 5.3 °C, 10.2% and 0.78% and 3.45 °C, 7.7% and 0.86%, respectively. Keywords: Semi-solar greenhouse, Dynamic model, Commercial greenhous

A comparative thermodynamic analysis of ORC and Kalina cycles for waste heat recovery: A case study for CGAM cogeneration system

A thermodynamic modeling and optimization is carried out to compare the advantages and disadvantages of organic Rankine cycle (ORC) and Kalina cycle (KC) as a bottoming cycle for waste heat recovery from CGAM cogeneration system. Thermodynamic models for combined CGAM/ORC and CGAM/KC systems are performed and the effects of some decision variables on the energy and exergy efficiency and turbine size parameter of the combined systems are investigated. Solving simulation equations and optimization process have been done using direct search method by EES software. It is observed that at the optimum pressure ratio of air compressor, produced power of bottoming cycles has minimum values. Also, evaporator pressure optimizes the performance of cycle, but this optimum pressure level in ORC (11 bar) is much lower than that of Kalina (46 bar). In addition, ORC's simpler configuration, higher net produced power and superheated turbine outlet flow, which leads to a reliable performance for turbine, are other advantages of ORC. Kalina turbine size parameter is lower than that of the ORC which is a positive aspect of Kalina cycle. However, by a comprehensive comparison between Kalina and ORC, it is concluded that the ORC has significant privileges for waste heat recovery in this case

An Evaluation of the Performance of Forced Air Cooling on Cooling Parameters in Transient Heat Transfer at Different Layers of Pomegranate

The quality of horticultural products can be promoted using high techniques. One of these methods is precooling applied before storage and leads to increased shelf and storage life of the fruit. For this reason, the effect of forced air cooling was conducted to investigate the cooling rate at the center (aril), spongy tissue (peel) and leathery skin (rind) of pomegranate (Punica granatum L.). Airflow velocity as an effective factor in cooling products at three levels of 0.5, 1, and 1.3 m s-1 and temperature of 7.2 °C was considered. Cooling parameters including lag factor and cooling coefficient were calculated from experimental data. Then, half-cooling time and seven-eighths cooling time were obtained at different layers of pomegranate. Cooling heterogeneity was analyzed at different air velocity and at different layers of pomegranate. The results showed that increase in air velocity from 0.5 to 1.3 m s-1, reduced the half-cooling time and seven-eighths cooling time. After 5000 seconds, the change of air velocity had a slight influence on decreasing temperature of different layers of pomegranate. Cooling heterogeneity at the air velocity of 0.5 m s-1 was low and then increased at the air velocity of 1 m s-1. Finally, at the air velocity of 1.3 m s-1, it was declined. The overall results illustrate that the applied methodology in this research, which explains unsteady heat transfer in the cooling process, can be performed in pomegranate or similarly shaped fruits.&nbsp

Numerical investigation of increasing the efficiency of thermal photovoltaic system by changing the level of heat transfer distribution

In photovoltaic systems, only a small fraction of solar radiation effectively reaches the module's surface and is converted into electrical energy. The unused solar radiation results in elevated cell temperature and reduced electrical efficiency. In the photovoltaic-thermal system, the circulation of fluids such as water or air around the panels allows for the utilization of otherwise wasted thermal energy, leading to increased electrical efficiency and overall system efficiency. The objective of this article is to examine the thermal efficiency of copper and aluminum oxides in photovoltaic systems when combined with nanofluid. The aim is to identify any changes in efficiency that may arise from this combination. This article presents a novel approach by employing a combination of aluminum oxide and copper nanofluids, along with a water mixture, to investigate the influence of key factors on the electrical, thermal, and overall efficiency of photovoltaic systems. These factors encompass incoming radiation levels on the panel surface, fluid inlet temperature in mountainous regions, and absorber temperature. The study aims to analyze and compare the thermal efficiency of copper and aluminum oxide in this particular context. In the present study, the finite volume method is used to solve the equations. According to the research findings, raising the initial temperature of the incoming fluid results in a proportional increase in the outlet temperature. However, it is important to note that the thermal efficiency remains constant regardless of changes in the initial fluid temperature. Furthermore, copper oxide exhibits superior thermal efficiency compared to aluminum oxide, and the use of nanofluids enhances the thermal efficiency of photovoltaic systems

Applying feature selection and machine learning techniques to estimate the biomass higher heating value

Abstract The biomass higher heating value (HHV) is an important thermal property that determines the amount of recoverable energy from agriculture byproducts. Precise laboratory measurement or accurate prediction of the HHV is essential for designing biomass conversion equipment. The current study combines feature selection scenarios and machine learning tools to establish a general model for estimating biomass HHV. Multiple linear regression and Pearson’s correlation coefficients justified that volatile matter, nitrogen, and oxygen content of biomass samples have a slight effect on the HHV and it is better to ignore them during the HHV modeling. Then, the prediction performance of random forest, multilayer and cascade feedforward neural networks, group method of data handling, and least-squares support vector regressor are compared to determine the intelligent estimator with the highest accuracy toward biomass HHV prediction. The ranking test shows that the multilayer perceptron neural network better predicts the HHV of 532 biomass samples than the other intelligent models. This model presents the outstanding absolute average relative error of 2.75% and 3.12% and regression coefficients of 0.9500 and 0.9418 in the learning and testing stages. The model performance is also superior to a recurrent neural network which was recently developed in the literature using the same databank

Effects of citalopram on heart rate variability in women with generalized anxiety disorder

BACKGROUND: Heart rate variability (HRV) is defined as variations in R-R interval with time. Dysautonomia is common in patients with psychiatric disorders such as depression and anxiety. Using HRV analysis, recent studies showed that in anxiety disorders, the vagal cardiac function decreases, and sympathetic function increases. This study aimed at investigating citalopram effects on HRV. METHODS: This before and after study was conducted in 25 generalized anxiety disorder (GAD) patients. GAD was diagnosed based on clinical interview according to diagnostic and statistical manual of mental disorders IV-Text revised (DSM-IV-TR) criteria using Structured Clinical Interview for DSM Disorders-I questionnaire. A cardiologist studied 24 h ambulatory monitoring of the electrocardiogram (Holter) on all patients before the treatment. A volume of 20 mg of citalopram was administered to the subjects on a daily basis. Then, they were studied by Holter monitoring again after 1-month of administration of citalopram. RESULTS: The average age of participants was 35.32 &plusmn; 8.7. The average Holter monitoring time was 23.29 &plusmn; 1.14 h before treatment and 23.81 &plusmn; 0.68 after it. The 3 h low frequency/high frequency ratio was significantly different between 3 h segments of time before treatment (P &lt; 0.001). This difference was even higher after treatment (P = 0.001). Data showed an increase in parasympathetic tone during sleep both before and after treatment. CONCLUSION: These patients showed some impairments of HRV indices that did not improve by citalopram in future, the clinical importance of such disturbances should be evaluated in details with prolonged follow-up and greater sample size. &nbsp;&nbsp;</div

Comparison of single and multi-coolant axial jets on the temperature reduction of nose cone with multiple-row disk

Ensuring the thermal safety of the nose cone on high-velocity shuttles is crucial for maintaining the overall safety of hypervelocity vehicles. This study investigated the use of axial single and multiple jets to reduce the thermal loading on the main structure of the nose cone, specifically through the implementation of a multi-row disk. The research focused on analyzing the flow structure and shock interaction near the spike assembly by introducing a coolant sonic jet. Computational fluid dynamics was employed to simulate the compressible flow around the nose cone at a hypersonic flow regime, and the study comprehensively examined the impact of the jet position on the shock interactions and cooling mechanism of the spike and nose cone. The results specify that injecting the tip of the spike with a multiple-row disk is more effective in protecting the blunt body from thermal damage at hypersonic flow. Moreover, a comparison between single and equivalent multiple-jet systems revealed that the thermal performance of the multi-jet configuration is significantly superior to that of a single jet