The Economic and Environmental Impact of Greenhouse Heating Pipe Insulation

Küçüktopcu, Erdem; Cemek, Bilal; Simsek, Halis

doi:10.3390/su14010549

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…Study [9] aimed to determine the influence of the optimal thickness of pipe insulation on energy saving and air pollution in greenhouse conditions. In this regard, an optimization model based on life cycle cost (LCC) analysis was carried out using the P1-P2 method.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Determining thermal and physical characteristics of wood polymer material for pipeline thermal insulation

Tsapko,

Kasianchuk,

Likhnyovskyi

et al. 2023

EEJET

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One of the methods for ensuring the efficiency of pipelines for transporting heat-carriers during operation is their thermal insulation, which inhibits heat transfer processes and does not affect environmental indicators. Therefore, the object of research was wood, a polymer material made by polymerization of wood sawdust and dry mixtures of synthetic resins for thermal insulation of pipelines. It has been proven that in the process of thermal action on the heat insulating layer of wood polymer material, the process of heat inhibition involves the formation of pores. This is due to the fact that the thermal conductivity of the material depends on the volumetric mass, the decrease of which for a wood polymer product leads to a decrease in thermal conductivity. In this regard, the simulation of the process of heat transfer through a cylindrical heat-insulating layer made of wood of polymer material was carried out and the dependences derived, which allow obtaining a change in the dynamics of heat transfer and determining thermophysical properties. According to the experimental data and the established dependences, it was found that the thermal conductivity of the wood of the polymer material was within 2.4÷2.9·10-8 m2/s, the thermal conductivity of the sample did not exceed 0.030 W/(m∙K). In addition, the heat capacity of the product corresponds to a value of more than 1034÷1145 kJ/(kg·K) depending on the thickness, which accordingly categorizes it as a heat-insulating material. At the same time, data on thermal insulation properties for polyurethane foam show that when it is used with a density of 100 kg/m3, the thermal conductivity is 0.029 W/(m∙K), which is approximately the same as the value of the proposed wood polymer material. The practical value is the fact that the results of determining the heat-insulating properties of a wood polymer material make it possible to establish the scope and conditions of its application

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Taking into account the low convergence of the series (9) and after substituting equations ( 7) and ( 8) into equation (9), we obtain the expression:…”

Section: Modeling Parameters Of the Heat Exchange Process Of Wood Pol...mentioning

confidence: 99%

Determining thermal and physical characteristics of wood polymer material for pipeline thermal insulation

Tsapko,

Kasianchuk,

Likhnyovskyi

et al. 2023

EEJET

View full text Add to dashboard Cite

show abstract

“…A number of studies also indicated that the performance of hybrid forecasting models outperformed that of single models, and the forecast accuracy was greatly improved by hybrid models [23][24][25]. For example, in Atakum, Turkey, a hybrid model was constructed for ET 0 forecast based on the autoregressive integrated moving average model and generalized regression neural networks, and the hybrid model efectively improved ET 0 forecast accuracy [26]. In Brazil, a hybrid model was established for ET 0 forecast based on support vector machine and artifcial neural network models, and the results showed that the hybrid model had the highest ET 0 forecast efciency and accuracy [27].…”

Section: Introductionmentioning

confidence: 99%

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Qin,

Fan,

Zhang

2024

Advances in Meteorology

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Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain.

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“…These methods are often complex, nonlinear, influenced by random factors, and rely on multiple assumptions. Each method is optimized based on the specific characteristics and unique weather conditions of the studied area (Küçüktopcu, 2023). However, experimental methods for measuring ET are limited to field or catchment-level applications.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

Amani,

Shafizadeh-Moghadam,

Morid

2024

Water Resour Manage

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The current study evaluated the accuracy of four machine learning (ML) techniques and thirteen experimental methods calibrated to estimate potential evapotranspiration (ET0) in arid and semi-arid regions. Various scenarios utilizing meteorological data were examined, and FAO56-PM was used as a benchmark. The results revealed that the ML models outperformed the experimental methods at both daily and monthly scales. Among the ML models, the artificial neural networks (ANNs), generalized additive model (GAM), random forest (RF), and support vector machine (SVM), respectively, demonstrated higher accuracy on a monthly scale, while the ANNs, SVM, RF, and GAM exhibited greater accuracy on a daily scale.Notably, the ANNs and SVM achieved high accuracy even with a limited number of variables.Conversely, the accuracy of the RF improved with an increased number of variables.Comparing ML models to experimental models with equivalent input revealed that ANN with inputs similar to Valiantras-1 performed better on a monthly scale, while SVM with inputs akin to Valiantras-3 showed superior performance on a daily scale. Our findings suggest that average temperature, wind speed, and sunshine hours contribute significantly to the accuracy of ML models. Consequently, these ML models can serve as an alternative to the FAO56-PM method for estimating ET0.

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The Economic and Environmental Impact of Greenhouse Heating Pipe Insulation

Cited by 4 publications

References 32 publications

Determining thermal and physical characteristics of wood polymer material for pipeline thermal insulation

Determining thermal and physical characteristics of wood polymer material for pipeline thermal insulation

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

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