Thermal Efficiency Model Research Articles

Building thermal energy efficiency modeling based on light image inspection and super resolution algorithm in interior landscape design

With the intensification of global climate change and energy crisis, the thermal efficiency of buildings has become increasingly important. Interior landscape design not only affects living comfort, but also plays a key role in building energy efficiency. Therefore, it is of great practical significance to explore the modeling method of building thermal energy efficiency based on optical image inspection and super resolution algorithm. The light image inspection technique is used to obtain the data of light and temperature distribution inside the building. The super-resolution algorithm of deep learning is used to process the acquired low-resolution image to improve the clarity and detail of the image. Combined with the physical characteristics of the building, the thermal efficiency model of the building is constructed and multi-dimensional analysis is carried out. The experimental results show that the combination of optical image inspection and super resolution algorithm can effectively improve the accuracy of building thermal efficiency modeling. Compared with traditional methods, the prediction error of the model is reduced, and the recommended optimization scheme performs better in terms of energy consumption in different interior landscape design schemes. Therefore, the building thermal efficiency modeling method based on optical image inspection and super resolution algorithm provides a new idea for interior landscape design. Through accurate thermal efficiency analysis, it can provide more scientific decision-making basis for architectural designers, so as to realize the sustainable development of buildings and maximize the energy efficiency.

Data Optimization Analysis of Integrated Energy System Based on K -Means Algorithm

To learn about the practical application of K -environment algorithms in electronic data analysis. To increase the thermal efficiency of boiler combustion and reduce nitrogen oxide emissions, the paper uses a 300 MW circulating liquid bed boiler for a thermal power plant as a research product. The studied and improved optimization methods have been successfully used to optimize the combustion of circulating liquefied boilers. Based on the advantages and disadvantages of biogeographic optimization algorithm and K -means clustering algorithm, this paper combines the two algorithms into a new improved clustering algorithm k -bbo-cluster. According to the operation mode of circulating fluidized bed boiler, the calculation method of boiler combustion thermal efficiency and the generation mechanism of nitrogen oxides, the boiler thermal efficiency model, nitrogen oxide emission concentration model and its comprehensive model are established by using the least square support vector machine method based on Bayesian structure framework. The learning outcomes of the vector machines that support the minimum squares of the Bayesian structure are less than 0.05 by the difference between MSE, MAE, and MAPE. The study of optimizing the combustion of circulating liquefied bed furnaces in this article can effectively improve the thermal efficiency of circulating liquefied bed furnaces and reduce nitrogen oxide emissions. Protection is important.

Optimal operating strategy of ash deposit removal system to maximize boiler efficiency using CFD and a thermal transfer efficiency model

Ash deposits generated during the combustion process on the outer tube surface of a boiler reduces the boiler’s efficiency. In most boilers, ash deposits are physically removed with high-pressure steam using an ash deposit removal system, sootblower. However, most sootblowers spray steam with excessive pressure, without considering the characteristics of the ash deposit, resulting in problems such as waste of steam energy and damage to equipment. In addition, an optimal operation strategy based on the position of the sootblower and the injection angle of the steam nozzle are not considered, leading to a lowered efficiency of ash deposit removal and steam waste. Hence, this study proposes a novel method to determine the optimal operating conditions of a sootblower using computational fluid dynamics (CFD) and a thermal transfer efficiency model considering the properties of the ash deposits. This method was developed based on a commercial recovery boiler, and consists of (i) a sootblowing steam velocity model that considers the ash deposit characteristics, (ii) a three-dimensional CFD model considering the interior design of the boiler, and (iii) a thermal transfer efficiency model that calculates the thermal resistance depending on the amount of ash deposit removed.Case studies were performed to calculate the amount of ash deposit removed for various ash deposit thicknesses, and sootblower positions and angles using this methodology. The boiler heat transfer efficiency was calculated using the calculated ash deposit removal amount and thermal transfer efficiency model, and the optimum operating conditions for the sootblower with the optimum heat transfer efficiency were derived. This study presents a guideline for efficiently operating sootblowers according to the ash deposit characteristics, and can be applied not only to recovery boilers but also to other boilers in which ash deposits are generated.

Performance improvement of a desiccant based cooling system by mitigation of non-uniform illumination on the coupled low concentrating photovoltaic thermal units

A Low Concentrating Photovoltaic Thermal system typically employs compound parabolic concentrator to focus sunlight and enhance the quality of both thermal and electrical energy extracted. One of the major issues during this process is the introduction of non-uniform illumination on the photovoltaic panels which can cause hot-spots and significantly reduce both the reliability and the electrical output from this system. This non-uniform illumination can be mitigated by integrating homogenizers which are typically linear extensions to the compound parabolic concentrators profile also referred to as elongated compound parabolic concentrators. In this work, the performance of a 2.5× Elongated Compound Parabolic Concentrator truncated to 1.7× and connected to a desiccant based cooling system has been explored. For a detailed analysis of the system, a coupled 3-D optical, electrical, thermal and process efficiency model has been developed. A full-scale prototype of the modelled system is also fabricated using a 380-Watt peak photovoltaic panel. Experiments conducted on the developed system showed a peak outlet water temperature of 56 °C at a mass flowrate of 24 L per hour. Comparative studies between compound parabolic concentrators and elongated compound parabolic concentrators based low concentrating photovoltaic thermal system is also presented to showcase the overall improvement in the process efficiency due to the mitigation of non-uniformity. Using a 400 mm length of the homogenizer the spatial non-uniformity factor was found to drop from 0.5 to 0.29 under normal incidence angle and results in a rise of 12% in the electrical output when compared to a compound parabolic concentrators-based system. The coefficient of performance of the desiccant-based air-cooling system is found to increase by 50% when coupled with two series-connected elongated compound parabolic concentrators based low concentrating photovoltaic thermal system. The improvement in coefficient of performance is mainly because of thermal and electrical energy savings from the developed system amounting to 352 kWhe/year and 665 kWhth/year, respectively. Further, the mitigation of non-uniform illumination showed a performance improvement of 5% in the coefficient of performance of the air-cooling system compared to a compound parabolic concentrators-based system.

Modeling Joule Heating Effect on Thermal Efficiency of Photovoltaic Thermal (PVT) Collectors with Operation Mode Factor (OMF)

The purpose of the present study is developing the operation mode factor (OMF) by remodeling the thermal efficiency model of a hybrid PVT collector during steady state. Joule heating occurs when the photovoltaic (PV) panel operates at a high current during maximum power point tracking (MPPT) on higher irradiation. Under these conditions, some electrical energy converts to thermal energy within the PV cells. Joule heating contributed to increasing the PVT thermal efficiency. The steps were to construct the OMF by remodeling the thermal efficiency involving the Joule heating effect and to validate the results using the model by comparing the simulation and experiment. The dimensionless OMF was responsible for changes in thermal efficiency for PVT-mode. The conductive heat transfer coefficient from the surface to the absorber was the most decisive component in the OMF. Heat removal factor and OMF might be interrelated at the mass flow rate by decreasing PV temperature to maintain Joule heating. The proposed model with OMF had explained PVT-mode and T-mode with the RMS value of less than 1%. This model complemented the results of the previous studies. The results may contribute from the initial design to the operational monitoring for thermal to electrical energy production.

Effect of Non-Convective Zone Thickness on Thermal Efficiency of Salt Gradient Solar Ponds

An improved radiation transmission and thermal efficiency model for solar ponds has been proposed based on both the Hull Model and Wang/Seyed-Yagoobi Model in this paper. The new model is more accurate to actual measured conditions because multiple reflections and turbidity effects are included. Absorption penetration, thermal conductivity loss and thermal efficiency under different Non-Convective Zone thicknesses are numerically analyzed and thoroughly discussed. The results show that ΔT/I0 plays a critical role for the thermal efficiency of solar pond. Furthermore, it is found through calculation that there is an optimum thickness of the Non-Convective Zone. When the Non-Convective Zone thickness is less than this critical threshold, both temperature and thermal efficiency are decreased with increasing turbidity. However, when the Non-Convective Zone thickness is greater than this critical threshold, the increasing turbidity within a certain range will be beneficial to improve the thermal efficiency of solar pond. In addition, optimum Non-Convective Zone thickness is also related to the temperature, turbidity, salinity variation and bottom reflectivity.

Modeling of the Thermal Efficiency of a Whole Cement Clinker Calcination System and Its Application on a 5000 MT/D Production Line

This paper proposes that the scope of research should be extended to the whole clinker calcination system from its single device or specific process (i.e., its functional subunits) as conventionally conducted. Mass/heat flow and effective heat were first analyzed to obtain the thermal efficiencies of its subunits (φi); a thermal efficiency model of the whole system φQY was thus established by correlating the relationship between φi and φQY. The thermal efficiency model of the whole system showed that φi had a positive linear correlation with φQY; it was found that the thermal efficiency of the decomposition and clinker calcination unit (φDC) had the greatest weight on φQY, where a 1% increase in φDC led to a 1.73% increase in φQY—improving φDC was shown to be the most effective way to improve φQY. In this paper, the developed thermal efficiency model was applied to one 5000 MT/D production line. It was found that its φQY was only 61.70%—about 2.35% lower than a representative line; such decrease was caused by its low φDC and φP which, as disclosed by model, were derived from the low decomposition rate of calcium carbonate in preheated meal put into a calciner and the high excess air coefficient of secondary air. Controlled parameter optimization of this 5000 MT/D production line was then carried out. As a result, the φDC and φP of the production line were increased from 30.03% and 64.61% to 30.69% and 65.69%, respectively; the φQY increased from 61.70% to 62.55%; the clinker output of the production line increased from 5799 MT/D to 5968 MT/D; the heat consumption of clinker was reduced from 3286.98 kJ/kg·cl to 3252.41 kJ/kg·cl.

Online simplified model and experimental comparison of CFB boiler thermal efficiency

In recent years, circulating fluidized bed boiler (CFB) combustion technology, which is used as a purification technology in coal combustion, has been widely studied. Some studies have conducted simplified investigations into calculating the standard boiler thermal efficiency. However, these studies are mainly aimed at simplified calculations of the boiler’s heat balance and data correction, and an online prediction model based on the core influential parameters have not been developed.To solve this problem, this study first calculates the boiler heat balance and thermal efficiency calculation to analyze thermal efficiency calculation factors. The exhaust heat loss q2 and the incomplete combustion heat loss of gas q3 decrease as the slag carbon content increases. For every 1% increase in the slag carbon content, the thermal efficiency is reduced by 1.95%; for every 5 °C increase in the exhaust gas temperature, the thermal efficiency is reduced by 0.26%.Then a simplified boiler thermal efficiency model, based on the slag carbon content and exhaust gas temperature determined to be the core parameters, is derived and compared with the measured results. The functional relationship is derived as follows: η=98.012–0.053tpy-1.944Clz (tpy < 200 °C, Clz < 2%).

Exergetic Analysis of Hybrid Photovoltaic - Thermal Solar Collectors Coupled to Organic Rankine Cycles

In this work, the application of hybrid solar modules that combine photovoltaic panels and solar thermal collectors coupled with a low-temperature thermal cycle such as the Organic Rankine Cycle is discussed, their main purpose being an increase in the total electric power production per available area. This work will study the thermal and electrical power production efficiency of the hybrid system, the increase in the PV module electric conversion efficiency due to their cooling through heat transfer to the thermal cycle and the total exergetic efficiency of the system. A simplified simulation of the system in steady state conditions based on a thermal efficiency model will be performed with the aid of the EES (Engineering Equation Solver) software using climate data from Campinas, São Paulo, Brazil. The study shows that while the PV/T+ORC system does fulfill the purpose of increasing the electrical power generation both from the generator coupled to the thermal cycle and from the increase in the PV module efficiency due to its cooling. Thus, there is an increase the overall exergy efficiency of the system compared to uncoupled PV/T collectors.

A methodology for predicting hybrid solar panel performance in different operating modes

Abstract When a hybrid solar panel produces thermal energy, it can operate in either hybrid mode, or thermal-only mode. In hybrid mode, the panel produces both electrical power and thermal power, and in thermal-only mode, only thermal power is produced. It has been shown that the thermal performance of a hybrid panel can vary by 15% on average between these two modes, but panel manufacturers are only required to publish performance data for one mode. Other studies in the literature have found a difference in panel thermal performance between these two modes, but they do not discuss a methodology to estimate alternate mode performance using manufacturer-supplied data. To alleviate this gap in the literature, this study presents a novel methodology to estimate alternate mode thermal performance of a hybrid solar panel only using manufacturer-supplied data. To match the panel information that is typically available, the second-order thermal efficiency model is used to estimate thermal performance, and temperature dependent electrical characteristics are used to estimate electrical performance. Indoor testing using a solar simulator was carried out, and the detailed test results are included. Results show that using the proposed modification technique can estimate thermal performance within 4% of actual values on average.

Towards working fluid properties and selection of Rankine cycle based waste heat recovery (WHR) systems for internal combustion engines – A fundamental analysis

Working fluid selection is one of the most important procedures in the design of Rankine cycle based waste heat recovery systems for internal combustion engines. The system’s performance, cost, and environmental impacts can be greatly influenced by the properties of its working fluid. In this work, we present an original formulation of a theoretical thermal efficiency model to fundamentally understand how the working fluid properties affect the thermal performance of the Rankine cycle based waste heat recovery systems for internal combustion engines. The derived theoretical thermal efficiency formula is validated under several different operating conditions. Overall very good agreements are achieved between the actual and theoretical approximated solutions. The results show that when the evaporation and condensation temperatures of the waste heat recovery system are fixed, the system’s thermal efficiency is simply governed by a non-dimensionalized parameter Jakob number. A working fluid with a smaller Jakob number is preferable in terms of system thermal efficiency. The observation is also further understood from a more fundamental physical perspective. Finally, a preliminary working fluid selection approach with a solid physics is proposed to screen the fluid candidates from the perspective of system performance.

A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency

The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm. To improve the solution quality and convergence speed of FPA, we proposed a novel flower pollination algorithm (NFPA) which is a hybrid algorithm based on original FPA and wind driven optimization algorithm. Simulation experiments demonstrate that NFPA has better search performance on classical numerical function optimizations compared with other the state-of-the-art optimization methods. In addition, the NFPA is adopted to optimize parameters of fast learning network to build thermal efficiency model of a 330 MW coal-fired boiler and a well-generalized model is obtained. Experimental results show that the tuned fast learning network model by NFPA has better prediction accuracy and generalization ability than other combination models.

Bottleneck of slab thermal efficiency in reheating furnace based on energy apportionment model

To reveal distribution of slab thermal efficiency (STE) and its bottlenecks, a slab region thermal efficiency (SRTE) model and an STE model were established based on the energy apportionment model of a reheating furnace in this paper. The bottleneck index of the slab thermal efficiency (BISTE), which could be used to assess the influence of SRTE on STE in a particular region, was proposed. First, the regional energy balance equation was listed based on reheating furnace region division. Next, the SRTE and STE models were established. Second, the bottleneck of slab thermal efficiency (BSTE) was achieved through a partial correlation analysis (PCA) of billet samples, which were obtained according to the difference between billet loading temperature and its residence time in the reheating furnace. Next, the BISTE was advanced to accurately determine the BSTE. Finally, several suggestions or measures, that could improve SRTE, were proposed. The case study has demonstrated the validation of these models, and the BISTE was 42% (Preheating), 19% (Heating II), 18% (Soaking), 11% (Heating I) and 10% (Preheating & Heating), respectively. Therefore, the preheating zone is the key region used to improve STE.

Research and application of quantum-inspired double parallel feed-forward neural network

A novel artificial neural network called fast learning network (FLN) was proposed by Li et al. in 2014, which showed better performance than some famous traditional artificial neural network algorithms. Inspired by the conventional FLN and quantum mechanics, this work proposes a kind of quantum-inspired double parallel feed-forward neural network (QIDPFNN). Compared with the FLN, the proposed algorithm presents two obvious differentials. Firstly, the input weights between hidden layer and input layer are generated by quantum computing. Secondly, a new hidden layer activation function is introduced. In order to verify the QIDPFNN validity, it is applied to 13 regression applications. The experimental results show that the QIDPFNN owns better generalization ability and stronger stability than FLN on most applications. Simultaneously, the QIDPFNN is applied to build NOx emission concentration model and thermal efficiency model of a 330 MW circulating fluidized bed boiler. The experiment results demonstrate that the proposed method has high regression precision, strong stability and generalization ability.

Model NOx emission and thermal efficiency of CFBB based on an ameliorated extreme learning machine

Extreme learning machine (ELM) is a novel single hidden layer feed-forward network, which has become a research hotspot in various domains. Through in-depth analysis on ELM, there are four factors mainly affect its model performance, such as the input data, the input weights, the number of hidden layer nodes and the hidden layer activation function. In order to enhance the performance of ELM, an ameliorated extreme learning machine, namely AELM, is proposed based on the aforementioned four factors. The proposed method owns new way to generate input weights and bias of hidden layer and has a new-type hidden layer activation function. Simulations on many UCI benchmark regression problems have demonstrated that the AELM generally outperforms the original ELM as well as several variants of ELM. Simultaneously, the AELM is adopted to build thermal efficiency model and NOx emission model of a 330MW circulating fluidized bed boiler. The results demonstrate the AELM is a useful machine learning tool.

Improved coal combustion optimization model based on load balance and coal qualities

Combustion optimization by fine tuning combustion parameters of boilers can cut down NOx emissions effectively with little cost. Coal qualities, which change from time to time, have great influences on NOx emissions. However, the current NOx reduction optimization model cannot handle with this problem well. What is more, the output load would deviate from the demand load as the boiler efficiency is also affected by the optimized manipulated variables (MVs). In this paper, an on-line method to calculate coal qualities based on reverse balance thermal efficiency model was integrated into the optimization model. Furthermore, a new constraint was added to the optimization model to meet the demand load. NOx emission characteristics of some 600 MW capacity utility boiler were investigated. Fine selected MVs were taken as the inputs of support vector machines (SVM) and NOx emission was taken as the output, respectively. Parameters of SVM were fine tuned by particle swarm optimization algorithm (PSO). Combustion optimization for the studied boiler was undertaken based on the proposed optimization model. Results showed that the new model can provide lower NOx emissions and meet demand loads at the same time.

A Kind of Parameters Self-adjusting Extreme Learning Machine

Extreme learning machine (ELM) is a kind of feed-forward single hidden layer neural network, whose input weights and thresholds of hidden layers are generated randomly. Because the output-weights of ELM are calculated by the least-square method, the ELM presents a high speed on training and testing. However, the random input-weights and thresholds of hidden layers are not the best parameters, which can not pledge the training goals of the ELM to achieve the global minimum. In order to obtain the optimal input-weights and bias of hidden layer, this paper proposes the self-adjusting extreme learning machine, called SA-ELM. Based on the idea of the ameliorated teaching learning based optimization, the input-weights and the bias of hidden layer of extreme learning machine are adjusted with "teaching phase" and "learning phase" to minimize the objective function values. The SA-ELM is applied to the eight benchmark functions to test its validity and feasibility. Compared with ELM and fast learning network, the SA-ELM owns good regression accuracy and generalization performance. Besides, the SA-ELM is applied to build the thermal efficiency model of a 300 MW pulverized coal furnace. The experiment results reveal that the proposed algorithm owns engineering practical application value.

고분자 전해질 연료전지(PEMFC)의 운전을 통한 발전 및 열 생산 성능 예측 모델 개발

A fuel cell has come up as an upcoming new and renewable energy recently. The fuel cell is the system which is converting chemical energy to power and heat. When applied to fuel cell in buildings, PEMFC(Polymer electrolyte membrane fuel cell) is mainly used due to relatively low operating temperature (<TEX>$50-100^{\circ}C$</TEX>). Performance prediction models for fuel cell are actively being studied, however, researches are mainly focused on analysis about stack or electrochemical reaction in the fuel cell. The purpose of this study is to suggest empirical performance prediction model for PEMFC by statistically analyzing PEMFC operation data and to evaluate the feasibility of empirically derived models. Also, combining the data from this and previous study, electrical efficiency and thermal efficiency model are derived to completement each other. Fuel cell performance is predicted under any operating condition using these models.

A two-tier approach to the data-driven modeling on thermal efficiency of a BFG/coal co-firing boiler

Even though ratio of air to fuel is the major factor in determining the thermal efficiency of a boiler, how to optimize the ratio in the real operation is still more like art than science, especially for a gas/solid multi-fuel combustion system. This study, taking operation data from a real gas/solid fuels co-firing boiler, is aimed to develop the thermal efficiency model. To cope with the complexity of combustion mechanism in a co-firing boiler, this study proposed a two-tier approach to modeling thermal efficiency. The first tier is to transform the plant input variables into the derived variables, which are physically and statistically meaningful to the model-building. In the second tier, this study proposed an adaptive modeling approach by employing nonnegative garrote variable selection and auto-regression integrated moving average correction. Both approaches are first time proposed in building the thermal efficiency model for boilers. The prediction error of boiler thermal efficiency made by the acquired model reaches less than 0.03%. The high accuracy of the proposed modeling approaches makes the implementation of the model-based control of ratio of air to fuel for improving boiler’s efficiency readily practicable.

Efficiency and optimal performance evaluation of organic Rankine cycle for low grade waste heat power generation

This paper proposed a thermal efficiency model theoretically based on an ideal ORC to analyze the influence of working fluid properties on the thermal efficiency, the optimal operation condition and exergy destruction for various heat source temperatures were also evaluated utilizing pinch point analysis and exergy analysis. The proposed model exhibits excellent agreements with the theoretical data and shows better performance than the existing models. It also indicates that Jacob number and the ratio of evaporating temperature and condensing temperature have mainly influence on the thermal efficiency of ORC and low Jacob number shows attractive performance for a given operation condition. It is unadvisable to always pursuit of high thermal efficiency for low grade waste heat. According to the evaluation of optimal operation condition, different working fluids have little impact on the optimal operation condition of ORC and selection of working fluid reasonably based on heat source temperature will help to optimize the ORC performance. Working fluid with low critical temperature, low specific liquid heat and high vaporization latent is particularly well adapted for utilization in the ORC. Exergy analysis indicates that the evaporator contributes the major exergy destruction while the condenser has the smallest except the pump.