Search Results (51)

The solar thermal collector network (SCN) and the thermal energy storage system (TES) represent 90% of the solar thermal installation (STI) total costs. STI occupies 30 hectares, and any reduction is significant for the environment. The proposed approach, which includes a solar thermal plant with recirculation, a mixer, and a heat exchanger, reduces investment costs and environmental impact. It facilitates mixing in a simple tank. The developed methodology reduces the number of collectors and the size of the storage system. An industrial-powdered milk process is the case study. Two scenarios and the base case were evaluated. The four seasons and critical meteorological conditions were considered. Scenario one, without a heat exchanger, presents energy surpluses in three seasons. The second scenario, with a heat exchanger, heats the feedwater and guarantees the heat load and target temperature on critical days of the year. In this second scenario, it is possible to reduce the tank filling time from 8 to 7 h. Up to five parallels were reduced in both scenarios, with mass flow of 0.125 kg/s and up to 3.75% of the total tank volume of 52.65 m³ (mass flow 0.075 kg/s). The optimized system is cost-effective, and 10.20% of the total cost was reduced. This methodology can be applied to any low-temperature STI. Full article

In this study, an Artificial Neural Network (ANN) approach was employed to predict the outlet air temperature and thermal efficiency of a solar air collector equipped with porous absorber surfaces. The experimental data used for model development were obtained from a custom-built solar air collector whose absorber surface was constructed using porous metallic scourers. Three different absorber surface configurations were tested under varying operating conditions. The dataset included measurements of inlet air temperature, solar irradiance, air mass flow rate, and surface temperatures recorded at four distinct points on the absorber. Corresponding outlet air temperatures and thermal efficiency values were also determined experimentally. ANN models were trained using this dataset, and the prediction results were graphically compared with experimental outcomes for all three surface types. To further evaluate the model’s performance, test data were utilized, and the results were assessed using the correlation coefficient (R) and mean squared error (MSE) metrics. The ANN model demonstrated high predictive accuracy, yielding an R value of 0.99987 and an MSE of 0.0901. Full article

The intermittency and fluctuation of solar irradiation pose challenges to the stable control of PTC collector loops. Therefore, this study proposes an Artificial Neural Network-based Feedforward-Feedback (ANN-FF-FB) model, which integrates irradiation prediction, feedforward, and feedback regulation to form a composite control strategy for the solar collecting system. During step changes in solar irradiation intensity, this model can quickly and stably adjust the outlet temperature, with a response time one-quarter that of a conventional PID model, a maximum overshoot of only 0.5 °C, a steady-state error of 0.02 °C, and it effectively reduces the entropy production in the transient process, improving the thermodynamic performance. Additionally, the ANN-FF-FB model’s response time during setpoint temperature adjustment is one-third that of the PID model, with a steady-state error of 0.03 °C. Ultimately, the system temperature stabilizes at 393 °C, with efficiency increasing to 0.212, and the overshoot being less than 1 °C. Full article

Within the context of “peak carbon and carbon neutrality”, reducing carbon emissions from coal-fired power plants and increasing the proportion of renewable energy in electricity generation have become critical issues in the transition to renewable energy. Based on the principles of cascaded energy utilization, this paper improves the coupling methodology of an integrated solar thermal and coal-fired power generation system based on existing research. A parabolic trough collector field and a three-tank molten salt thermal energy storage system are connected in series and then in parallel with the outlet of the reheater. ASPEN PLUS V14 and MATLAB R2018b software were used to simulate a steady-state model and numerical model, respectively, so as to study the feasibility of the improved complementary framework in enhancing the peak load capacity of coal-fired units and reducing their carbon emissions. Actual solar radiation data from a specific location in Inner Mongolia were gathered to train a neural network predictive model. Then, the peak-shaving performance of the complementary system in matching load demands under varying hours of thermal energy storage was simulated. The findings demonstrate that, under constant boiler load conditions, optimizing the complementary system with a thermal energy storage duration of 5 h and 50 min results in an energy utilization efficiency of 88.82%, accompanied by a daily reduction in coal consumption by 36.49 tonnes. This indicates that when operated under the improved coupling framework with optimal parameters, the peak regulation capabilities of coal-fired power units can be improved and carbon emission can be reduced. Full article

Due to the growing popularity of vacuum tube solar collectors and their more esthetically pleasing look, horizontal hot water tanks are increasingly being used in solar hot water systems. In order to improve the thermal performance of a horizontal mantled hot water tank, this work numerically examines the impact of positioning inclination barriers parallel or coincident to one another at varying angles. The main input provided the velocity V = 0.036, 0.073, 0.11, and 0.147 m/s, and analysis were performed for each speed. The study concluded that V = 0.073 m/s was the ideal mains input velocity for each scenario and that raising the speed typically resulted in a lower mains outlet temperature. According to the study’s findings, the tank design with the first obstacle 150 mm away and the two obstacles 100 mm apart achieves the best efficiency. The residential water temperature in this model is 312 K, while the storage water temperature is 309.5 K. In this study, a feed-forward artificial neural network (ANN) model based predictor was designed to estimate the mantle outlet and main outlet temperatures and the temperature of the stored water. Analyses were performed for different network inlet velocities and obstacle combinations, and ANN showed superior performance in estimating temperature parameters. Full article

For automobile and aerospace engineers, implementing Hall currents and thermal radiation in cooling systems helps increase the performance and durability of an engine. In the case of solar energy systems, the effectiveness of heat exchangers and solar collectors can be enhanced by the best use of hybrid nanofluids and the implementation of a Hall current, thermophoresis, Brownian motion, a heat source/sink, and thermal radiation in a time-dependent hybrid nanofluid flow over a disk for a Bayesian regularization ANN backpropagation algorithm. In the current physical model of Cobalt ferrite $CoF{e}_2{O}_4$ and aluminum oxide $A{l}_2{O}_3$ mixed with water, a new category of the nanofluid is called the hybrid nanofluid. The study uses MATLAB bvp4c to unravel such intricate relations, transforming PDEs into ODEs. This analysis enables the numerical solution of several BVPs that govern the system of the given problem. Hall currents resulting from the interaction between magnetic fields and the electrically conducting nanofluid, and thermal radiation as an energy transfer mechanism operating through absorption and emission, are central factors for controlling these fluids for use in various fields. The graphical interpretation assists in demonstrating the character of new parameters. The heat source/sink parameter is advantageous to thermal layering, but using a high Schmidt number limits the mass transfer. Additionally, a backpropagation technique with Bayesian regularization is intended for solving ordinary differential equations. Training state, performance, error histograms, and regression demonstration are used to analyze the output of the neural network. In addition to this, there is a decrease in the fluid velocity as magnetic parameter values decrease and a rise in the fluid temperature while the disk is spinning. Thermal radiation adds another level to the thermal behavior by altering how the hybrid nanofluid receives, emits, and allows heat to pass through it. Full article

In order to reach climate protection goals at national or international levels, new forms of combined heating and cooling networks with ultra-low network temperatures (5GDHC) are viable alternatives to conventional heating networks. This paper presents a simulation library for 5GDHC networks as sustainable shared energy systems, developed in the object-oriented simulation framework OpenModelica. It comprises sub-models for residential buildings acting as prosumers in the network, with additional roof-mounted thermal systems, dynamic thermo-hydraulic representations of distribution pipes and storage, time-series-based sources for heating and cooling, and weather conditions adjustable to user-specified locations. A detailed insight into an in-house development of a sub-model for horizontal ground heat collectors is given. This sub-model is directly coupled with thermo-hydraulic network simulations. The simulation results of energy balances and energetic efficiencies for an example district are described. Findings from this study show that decentralised roof-mounted solar thermal systems coupled to the network can contribute 21% to the total source heat provided in the network while annual thermal gains from the distribution pipes add up to more than 18% within the described settings. The presented simulation library can support conceptual and advanced planning phases for renewable heating and cooling supply structures based on environmental sources. Full article

This study addresses the challenge of optimizing flat-plate solar collector design, traditionally reliant on trial-and-error and simplified engineering design methods. We propose using physics-informed neural networks (PINNs) to predict optimal design conditions in a range of data that not only characterized the highlands of Ecuador but also similar geographical locations. The model integrates three interconnected neural networks to predict global collector efficiency by considering atmospheric, geometric, and physical variables, including overall loss coefficient, efficiency factors, outlet fluid temperature, and useful heat gain. The PINNs model surpasses traditional simplified thermodynamic equations employed in engineering design by effectively integrating thermodynamic principles with data-driven insights, offering more accurate modeling of nonlinear phenomena. This approach enhances the precision of solar collector performance predictions, making it particularly valuable for optimizing designs in Ecuador’s highlands and similar regions with unique climatic conditions. The ANN predicted a collector overall loss coefficient of 5.199 W/(m²·K), closely matching the thermodynamic model’s 5.189 W/(m²·K), with similar accuracy in collector useful energy gain (722.85 W) and global collector efficiency (33.68%). Although the PINNs model showed minor discrepancies in certain parameters, it outperformed traditional methods in capturing the complex, nonlinear behavior of the data set, especially in predicting outlet fluid temperature (55.05 °C vs. 67.22 °C). Full article

One effective approach to reducing the energy required for heating buildings is the use of active thermal insulation (ATI). This method involves delivering low-temperature heat to the exterior walls through a network of pipes carrying water. For ATI to be cost-effective, the energy supply must be affordable and is typically derived from geothermal or solar sources. Solar energy, in particular, requires thermal energy storage (TES) to manage the gap between summer and the heating season. A building that integrates various renewable energy systems and heating/cooling technologies should be managed efficiently and sustainably. The proper integration of these systems with smart management strategies can significantly lower a building’s carbon footprint and operational costs. This study analyzes the use of concrete slabs as a method for sustainable energy management in a system incorporating active thermal insulation and solar collectors. Using ambient temperature and solar radiation data specific to Cracow, Poland, the simulations evaluate the feasibility of employing a concrete slab positioned beneath the building as a thermal storage tank. The results reveal some drawbacks of using concrete slabs, including high temperatures that negatively affect system efficiency. Increased temperatures lead to higher heat losses, and during summer, inadequate insulation can cause additional heat leakage into the building. The findings suggest that water may be a more effective alternative for thermal energy storage. Full article

Employing sustainable energy systems is a must fact of the current years. Urban districts can lead the decarbonization process of cities to allow the development of decentralization energy systems such as district heating. On the other hand, the exergy analysis combined with energy evaluation can be a suitable way to investigate the efficiency and flexibility of an energy system. In this framework, this study investigates the optimal energy and storage systems to feed a district heating network. Four types of energy systems were analyzed, such as boilers, cogeneration plants, solar systems and the combination of them. The size of the thermal energy storage of the network is investigated in terms of volume and temperature. In parallel, the exergy efficiency of all the systems was calculated. The optimal heating system configuration to feed the studied district heating is the cogeneration plant with solar collectors, according to both the temperature trend fluctuation and exergy efficiency of the system. Moreover, the employment of thermal energy storage is crucial to face the renewable energy source’s variability. As a further investigation, additional exergy indicators can be studied to underline the performances of such an decentralized energy system to increase the quality of the built environment. Full article

In pursuit of sustainable development and mitigation of the intermittency challenge associated with solar energy, this study proposes a hybrid solar system integrating waste heat incineration alongside solar power generation and distinct heat provision. Leveraging the superior energy efficiency of the organic Rankine cycle (ORC) in medium- and low-temperature scenarios, a parabolic trough collector (PTC) is selected for its cost-effectiveness and long-term operational reliability. Dowtherm A and toluene are identified as the optimal working fluids for the PTC and ORC, respectively. To optimize this complex system, a combination of artificial neural networks (ANNs) and multi-objective optimization via non-dominated sorting genetic algorithm II (NSGA-II) is employed, streamlining the optimization process. Thermal dynamic simulations are executed using Engineering Equation Solver (EES, V11) to validate the proposed system’s performance. TOPSIS is employed to identify the optimal solution from the Pareto frontier. The results indicate that the hourly cost of the system stands at USD 43.08, with an exergy efficiency of 22.98%. The economic analysis reveals that the solar collector constitutes the most significant portion of the total initial cost, representing 53.2%, followed by the turbine, thermoelectric generator (TEG), and waste heat incineration, in descending order of costliness. Full article

The design of a solar thermal installation is based on the lowest irradiance levels that occur during winter. However, there are consecutive days with irradiance levels well below those used for the design, which are called in this work “critical irradiance levels”. To solve this challenge, a statistical analysis is carried out to find a representative percentile of 22 years of consecutive days with “critical irradiance levels”. A case study of a cotton-dyeing industrial process requires 18.5 m³ of hot water and operates for 2.75 h at temperatures between 40 and 90 °C. Environmental variables for 22 years were analyzed and validated to design a solar thermal installation (solar collector network and storage system) and a coupled heat pump. The fifth percentile, with three consecutive days and low irradiance levels, was the most repetitive. For this case, a storage system of 46.5 m³ guaranteed heat load at target temperature. The simple payback was 14.1 years, and the energy cost was 0.094 USD/kWh, which was competitive against the energy cost from using fossil fuels, 0.064 USD/kWh. The design based on critical environmental conditions guarantees a continuous supply of energy to the industrial process and defines the minimum availability of solar energy to supply a process. Full article

A startling shift in waste composition has been brought on by a dramatic change in lifestyle, the quick expansion of consumerism brought on by fierce competition among producers of consumer goods, and revolutionary advances in the packaging sector. The overflow or overspill of garbage from the bins causes poison to the soil, and the total obliteration of waste generated in the area or city is unknown. It is challenging to pinpoint with accuracy the specific sort of garbage waste; predictive image classification is lagging, and the existing approach takes longer to identify the specific garbage. To overcome this problem, image classification is carried out using a modified ResNeXt model. By adding a new block known as the “horizontal and vertical block,” the proposed ResNeXt architecture expands on the ResNet architecture. Each parallel branch of the block has its own unique collection of convolutional layers. Before moving on to the next layer, these branches are concatenated together. The block’s main goal is to expand the network’s capacity without considerably raising the number of parameters. ResNeXt is able to capture a wider variety of features in the input image by using parallel branches with various filter sizes, which improves performance on image classification. Some extra dense and dropout layers have been added to the standard ResNeXt model to improve performance. In order to increase the effectiveness of the network connections and decrease the total size of the model, the model is pruned to make it smaller. The overall architecture is trained and tested using garbage images. The convolution neural Network is connected with a modified ResNeXt that is trained using images of metal, trash, and biodegradable, and ResNet 50 is trained using images of non-biodegradable, glass, and hazardous images in a parallel way. An input image is fed to the architecture, and the image classification is achieved simultaneously to identify the exact garbage within a short time with an accuracy of 98%. The achieved results of the suggested method are demonstrated to be superior to those of the deep learning models already in use when compared to a variety of existing deep learning models. The proposed model is implemented into the hardware by designing a three-component smart bin system. It has three separate bins; it collects biodegradable, non-biodegradable, and hazardous waste separately. The smart bin has an ultrasonic sensor to detect the level of the bin, a poisonous gas sensor, a stepper motor to open the lid of the bin, a solar panel for battery storage, a Raspberry Pi camera, and a Raspberry Pi board. The levels of the bin are maintained in a centralized system for future analysis processes. The architecture used in the proposed smart bin properly disposes of the mixed garbage waste in an eco-friendly manner and recovers as much wealth as possible. It also reduces manpower, saves time, ensures proper collection of garbage from the bins, and helps attain a clean environment. The model boosts performance to predict waste generation and classify it with an increased 98.9% accuracy, which is more than the existing system. Full article

The decarbonization of the district heating (DH) sector is receiving attention worldwide. Solar energy and heat pump technologies are widely considered in existing and new DH networks. There is a need to understand the influence of solar energy on district heating experimentally. However, only a few university laboratories are focused on district heating aspects. Further, the concept of such laboratories is not adequately disseminated in the scientific literature. The main objective of this paper is to develop a conceptual design of a solar energy laboratory with a focus on district heating systems. The proposed concept forms part of the preliminary study carried out by a research group at the Tallinn University of Technology. First, a brief literature review on solar energy laboratory development is provided. Then, the conceptual design of such a laboratory is presented, along with a case study. Regardless of project size, the main components of a district heating-based solar energy laboratory are solar collectors, thermal energy storage (TES) tanks, and a control system. The proposed laboratory is expected to serve multiple roles, such as a practical laboratory to provide interdisciplinary courses for students, a research and experimental platform for researchers, and a cradle to achieve the campus green initiative. It is roughly estimated that the thermal energy output from the proposed laboratory would meet around 25% of the heat demand of the institutional building during the summer season (May, June, July, and August). It is expected that the present study will be a reference material for the development of innovative energy laboratories in educational institutions. Full article

As the world’s third-largest oil and natural gas producer, Iran consumed enormous amounts of non-renewable energy during the last twenty years. There are many obsolete buildings in the Iranian building stock, which required energy renovation. Many studies in the literature proposed energy retrofitting strategies to increase the efficiency of buildings, but few of them involve an energy network for the entire neighbourhood (such as district heating). Moreover, energy renovation is not sufficient to improve the smartness level of a community; in fact, it is essential to evaluate sustainable and social aspects. In this direction, this study aims to develop a comprehensive analysis of the current criticalities of a district in Tehran (District 5), proposing strategies to face the pollution of the city, provide a healthy environment for the citizens, and renovate the old buildings. The application of a decision support method is presented to set a priority ranking, pointing out the positive and negative impacts of each evaluated scenario. The energy renovation solution involved the installation of two storage tanks and solar collectors in each building and the connection with the district heating powered by waste to the energy plant. A multi-level car parking system and a noise mapping application were evaluated to solve mobility and pollution problems. Moving to the results, the priority ranking assesses that the most affordable action is the installation of a Solar Water Heater since energy and environmental indicators demonstrate its efficacy compared to the other solutions. Full article