Research

15 pages, 7056 KiB

Open AccessArticle

Structural Optimization of Annular Thermoelectric Module Applied to Liquefied Natural Gas Cold Energy Recovery

by Yulong Zhao, Hongmei Diao, Wenjie Li, Zhiwei Xuan, Qi Zhang, Yulin Wang and Minghui Ge

Processes 2023, 11(9), 2687; https://doi.org/10.3390/pr11092687 - 07 Sep 2023

Viewed by 591

The gasification of liquefied natural gas (LNG) is characterized by a substantial release of cold energy, which can be utilized for power generation via thermoelectric generator (TEG). Employing a gasifier integrated with a thermoelectric generator for LNG gasification allows for the recovery of [...] Read more.

The gasification of liquefied natural gas (LNG) is characterized by a substantial release of cold energy, which can be utilized for power generation via thermoelectric generator (TEG). Employing a gasifier integrated with a thermoelectric generator for LNG gasification allows for the recovery of cold energy and its conversion to useful power, a process that holds significant potential for widespread application. In the study, a thermoelectric model has been developed for an annular thermoelectric module, which formed a new category of gasifier tube. The influence of the module’s structure as well as the heat transfer parameters on the thermoelectric performance was examined. The results revealed that an optimum height of the thermoelectric leg, specifically 2 mm, maximized the output power while allowing the thermoelectric conversion efficiency to reach a peak of 3.25%. Another noteworthy finding is that an increase in the central angle of the thermoelectric leg leads to a concomitant rise in output power but a decrease in conversion efficiency. Furthermore, when the heat transfer coefficients at the hot and cold ends of the module achieved 4000 W/(m²·K) and 10,000 W/(m²·K), respectively, the conversion efficiency can be elevated to 6.98%. However, any additional enhancement in power generation performance derived from further augmenting the heat transfer is marginal. These findings can serve as a valuable reference in the design and optimization of TEG intended for the recovery of cold energy from LNG. Full article

(This article belongs to the Special Issue Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes)

► Show Figures

Figure 1

20 pages, 4749 KiB

Open AccessArticle

First Law Optimization and Review of Double and Triple-Effect Parallel Flow Vapor Absorption Refrigeration Systems

by Md. Azhar

Processes 2023, 11(8), 2347; https://doi.org/10.3390/pr11082347 - 04 Aug 2023

Viewed by 693

Abstract

Parallel flow double and triple-effect vapor absorption cooling systems (VACS) are trying to meet the challenges of vapor compression cooling systems due to their better performance. Therefore, the present study deals with the review, thermodynamic analysis, and optimization of operating parameters for both [...] Read more.

Parallel flow double and triple-effect vapor absorption cooling systems (VACS) are trying to meet the challenges of vapor compression cooling systems due to their better performance. Therefore, the present study deals with the review, thermodynamic analysis, and optimization of operating parameters for both double and triple-effect VACS. Lithium bromide water was selected as the working fluid, while liquified petroleum gas (LPG) and compressed natural gas (CNG) were taken as the source of energy to drive both the VACS. Detailed First Law analysis, i.e., coefficient of performance (COP), was examined along with the optimization of operating parameters (such as salt concentration and operating generators temperature at different pressure levels) and the volume flow rate of the gases. Optimization was carried out for maximum COP of the VACS using an iterative technique. Our results show that the COP of the triple-effect system was approximately 32% higher than the double effect, while 15–20% less consumption of the gases (LPG and CNG) was observed. The most optimum stage for the operation of triple-effect VACS was reached at T_e = 4 °C and T_c = T_a = 30 °C, T_g = 180 °C, T_c4 = 104 °C, T_c3 = 66 °C, Z₁ = 0.5, and Z₂ = 0.45. Full article

(This article belongs to the Special Issue Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes)

► Show Figures

Figure 1

15 pages, 3280 KiB

Open AccessArticle

The Impact of Air Source Heat Pump on the Production Performance of Broiler Chicks

by Chenming Hu, Mohan Qiu, Chunlin Yu, Li Yang, Qubo Zhu, Anfang Liu, Longhuan Du and Chaowu Yang

Processes 2023, 11(5), 1360; https://doi.org/10.3390/pr11051360 - 28 Apr 2023

Viewed by 1121

Abstract

Air source heat pump (ASHP) is a good new energy heating system. To explore the effect of ASHP on the production of yellow-feather broiler chicks, 31,500 one-day-old yellow broiler chicks were divided into three chicken houses with the same building structure but different [...] Read more.

Air source heat pump (ASHP) is a good new energy heating system. To explore the effect of ASHP on the production of yellow-feather broiler chicks, 31,500 one-day-old yellow broiler chicks were divided into three chicken houses with the same building structure but different heating methods (ASHP, CCF, CB). During the experiment, the parameters of heating time, temperature uniformity, gas concentration, weight gain, survival rate and production benefit were analyzed and evaluated. Results showed that the difference in NH₃, CO₂, and H₂S concentrations was not significant in all test groups (p > 0.05). Only group II detected the CO gas. In winter and spring, the weight of the chickens in group II were weighed the least at 35 days of age, and were significantly different from the ASHP and CB system (p < 0.05). There was no significant difference in body weight between ASHP and CB (p > 0.05). Group II had the lowest evenness and survival, the slowest warming, the worst uniformity of temperature distribution, and the highest cost. It is concluded that the ASHP was very environmentally friendly and has the highest economy, which is worth promoting and using. Full article

(This article belongs to the Special Issue Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes)

► Show Figures

Figure 1

22 pages, 34274 KiB

Open AccessArticle

Investigation on the Dynamics of a Flexible Multi-Body System of a Three-Cylinder Gasoline Engine Crankshaft

by Xiao Zhang and Lu Zheng

Processes 2023, 11(4), 1248; https://doi.org/10.3390/pr11041248 - 18 Apr 2023

Cited by 1 | Viewed by 1443

Abstract

Three-cylinder gasoline engines are increasingly favored by major automobile manufacturers due to their good fuel economy, low manufacturing cost and low fuel consumption. However, the inherent balance problem has an adverse effect on the vibration of the whole engine and even the comfort [...] Read more.

Three-cylinder gasoline engines are increasingly favored by major automobile manufacturers due to their good fuel economy, low manufacturing cost and low fuel consumption. However, the inherent balance problem has an adverse effect on the vibration of the whole engine and even the comfort of the whole vehicle, which limits its application in high-end models. This paper studied the dynamics characteristic of the flexible multi-body system of the three-cylinder gasoline engine crankshaft. A dynamic simulation model of the flexible multi-body system of the three-cylinder gasoline engine crankshaft is established through the flexible treatment of the engine crankshaft. The kinematics and dynamics characteristics of each component of the crankshaft connecting rod system are obtained by analyzing the kinematics and dynamics characteristics of the engine shafting system. The relevant factors affecting the vibration of the engine crankshaft system are studied through the establishment of the analysis model of the torsional vibration of the engine crankshaft. This is of great significance to further improve and optimize the design of the three-cylinder gasoline engine. Full article

(This article belongs to the Special Issue Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes)

► Show Figures

Figure 1

15 pages, 1077 KiB

Open AccessArticle

Ensemble Machine-Learning Models for Accurate Prediction of Solar Irradiation in Bangladesh

by Md Shafiul Alam, Fahad Saleh Al-Ismail, Md Sarowar Hossain and Syed Masiur Rahman

Processes 2023, 11(3), 908; https://doi.org/10.3390/pr11030908 - 16 Mar 2023

Cited by 15 | Viewed by 2238

Abstract

Improved irradiance forecasting ensures precise solar power generation forecasts, resulting in smoother operation of the distribution grid. Empirical models are used to estimate irradiation using a wide range of data and specific national or regional parameters. In contrast, algorithms based on Artificial Intelligence [...] Read more.

Improved irradiance forecasting ensures precise solar power generation forecasts, resulting in smoother operation of the distribution grid. Empirical models are used to estimate irradiation using a wide range of data and specific national or regional parameters. In contrast, algorithms based on Artificial Intelligence (AI) are becoming increasingly popular and effective for estimating solar irradiance. Although there has been significant development in this area elsewhere, employing an AI model to investigate irradiance in Bangladesh is limited. This research forecasts solar radiation in Bangladesh using ensemble machine-learning models. The meteorological data collected from 32 stations contain maximum temperature, minimum temperature, total rain, humidity, sunshine, wind speed, cloud coverage, and irradiance. Ensemble machine-learning algorithms including Adaboost regression (ABR), gradient-boosting regression (GBR), random forest regression (RFR), and bagging regression (BR) are developed to predict solar irradiance. With the default parameters, the GBR provides the best performance as it has the lowest standard deviation of errors. Then, the important hyperparameters of the GRB are tuned with the grid-search algorithms to further improve the prediction accuracy. On the testing dataset, the optimized GBR has the highest coefficient of determination (

R^{2}

) performance, with a value of 0.9995. The same approach also has the lowest root mean squared error (0.0007), mean absolute percentage error (0.0052), and mean squared logarithmic error (0.0001), implying superior performance. The absolute error of the prediction lies within a narrow range, indicating good performance. Overall, ensemble machine-learning models are an effective method for forecasting irradiance in Bangladesh. They can attain high accuracy and robustness and give significant information for the assessment of solar energy resources. Full article

(This article belongs to the Special Issue Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes)

► Show Figures

Figure 1

19 pages, 2627 KiB

Open AccessArticle

Optimal Scheduling of Combined Electric and Heating Considering the Control Process of CHP Unit and Electric Boiler

by Yuehua Huang, Qing Chen, Zihao Zhang, Xingtao Liu, Jintong Tu and Lei Zhang

Processes 2023, 11(3), 753; https://doi.org/10.3390/pr11030753 - 03 Mar 2023

Cited by 1 | Viewed by 1203

Abstract

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology [...] Read more.

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric–thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control of CHP units and the retrofitting of electric boilers can promote the consumption of wind power and improve the overall operating economy of the system while ensuring the feasibility of the CEHS scheduling scheme. Full article

(This article belongs to the Special Issue Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes)

► Show Figures

Figure 1

28 pages, 5417 KiB

Open AccessArticle

Influence of Optimal Hyperparameters on the Performance of Machine Learning Algorithms for Predicting Heart Disease

by Ghulab Nabi Ahamad, Shafiullah, Hira Fatima, Imdadullah, S. M. Zakariya, Mohamed Abbas, Mohammed S. Alqahtani and Mohammed Usman

Processes 2023, 11(3), 734; https://doi.org/10.3390/pr11030734 - 01 Mar 2023

Cited by 13 | Viewed by 2284

Abstract

One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used [...] Read more.

One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparameters achieved the highest testing accuracy of 87.91% for dataset-I and the extreme gradient boosting classifier with tuned hyperparameters achieved the highest testing accuracy of 99.03% for the comprehensive dataset-II. The novelty of this work was the use of grid search cross-validation to enhance the performance in the form of training and testing. The ideal parameters for predicting heart disease were identified through experimental results. Comparative studies were also carried out with the existing studies focusing on the prediction of heart disease, where the approach used in this work significantly outperformed their results. Full article

(This article belongs to the Special Issue Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes)

► Show Figures

Figure 1

12 pages, 7430 KiB

Open AccessArticle

County-Based PM_2.5 Concentrations’ Prediction and Its Relationship with Urban Landscape Pattern

by Lijuan Yang, Shuai Wang, Xiujuan Hu and Tingting Shi

Processes 2023, 11(3), 704; https://doi.org/10.3390/pr11030704 - 26 Feb 2023

Cited by 2 | Viewed by 1082

Abstract

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM_2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model [...] Read more.

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM_2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM_2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM_2.5 values and landscape pattern. We achieved a cross validation R² of 0.91 (RMSE = 9.06 μg/m³), 0.89 (RMSE = 10.19 μg/m³), and 0.90 (RMSE = 8.02 μg/m³) between the estimated and observed PM_2.5 concentrations in 2016, 2018, and 2020, respectively. The PM_2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of “Jiangsu > Shanghai > Zhejiang > Fujian” in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM_2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM_2.5 (r = 0.84). There was also a significant correlation between the county-based PM_2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM_2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM_2.5 pollution in the study region. Full article

(This article belongs to the Special Issue Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Advanced Technologies and Materials for Sustainability in Energy Systems and Environmental Processes

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI