Heating System Research Articles

Design of a Thermal Performance Test Equipment for a High-Temperature and High-Pressure Heat Exchanger in an Aero-Engine

For next-generation power systems, particularly aero-gas turbine engines, ultra-light and highly efficient heat exchangers are considered key enabling technologies for realizing advanced cycles. Consequently, the development of efficient and accurate aero-engine heat exchanger test equipment is essential to support future gas turbine heat exchanger advancements. This paper presents the development of a high-pressure and high-temperature (HPHT) heat exchanger test facility designed for aero-engine heat exchangers. The maximum temperature and pressure of the test facility were configured to simulate the conditions of the last-stage compressor of a large civil engine, specifically 1000 K and 5.5 MPa. These conditions were achieved using multiple electric heater systems in conjunction with an air compression system consisting of three turbo compressor units and a reciprocating compressor unit. A commissioning test was conducted using a compact tubular heat exchanger, and the results indicate that the test facility operates stably and that the measured data closely align with the predicted performance of the heat exchanger. A commissioning test of the tubular heat exchanger showed a thermal imbalance of 1.02% between the high-pressure (HP) and low-pressure (LP) lines. This level of imbalance is consistent with the ISO standard uncertainty of ±2.3% for heat dissipation. In addition, CFD simulation results indicated an average deviation of approximately 1.4% in the low-pressure outlet temperature. The close alignment between experimental and CFD results confirms the theoretical reliability of the test bench. The HPHT thermal performance test facility will be expected to serve as a critical test bed for evaluating heat exchangers for current and future gas turbine applications.

Influence of the Building’s Thermal Insulation on Intermittent Heating Mode Efficiency

Abstract The results of mathematical modeling of a building’s thermal insulation influence on the efficiency of intermittent heating mode are presented. This work aim is to assess the influence of thermal insulation on the efficiency of using the intermittent heating mode for the educational building of the Odessa National Polytechnic University’s Heat Engineering Laboratory. The paper analyzes the factors related to improving the efficiency of the programmed heat supply mode. Modeling of the operating modes for heat supply system operated in intermittent heating mode is carried out. There was performed the mathematical modeling of the heating system operation modes for the most unfavorable climatic conditions at low outdoor temperatures, with respective system efficiency indicators obtaining. The potential of energy saving level for administrative, educational, and office buildings depending on the thickness of thermal insulation of external and internal walls is studied. Recommendations have been elaborated for the use of buildings thermal insulation to improve the efficiency of intermittent heating.

Optimizing Organic Rankine Cycle (ORC) configurations integrated with transient industrial waste heat: a multi-objective approach

Decarbonizing heat-intensive industries by reusing the waste heat for power or combined heat and power systems is becoming increasingly important to address global warming. The Organic Rankine Cycle has shown a high level of feasibility and performed efficiently for utilizing medium-to-low-grade heat from renewable resources and heat-intensive industries for direct power generation. This study contributes to the field by conducting a techno-economic investigation of various Organic Rankine Cycle configurations to enhance energy conversion when real-life transient waste heat sources are available. These configurations were optimized to maximize energy output along with economic benefits. The non-linear programming by quadratic Lagrangian, a computational unintensive yet accurate optimization algorithm, was utilized for the multi-objective optimization. The optimized cycle configurations showed a 12.57% enhancement of turbine efficiency. Combining regeneration and recuperation enhanced the superheating by 32%, and the optimized air preheater cycle improved the overall objective by 64.2% compared to the pre-optimized conventional cycle, leading to a feasible 1.72-year payback period.

Optimal operating strategy of hybrid heat pump − boiler systems with photovoltaics and battery storage

The growing need to reduce energy consumption and greenhouse gas emissions is driving the search for more efficient heating solutions in buildings. Hybrid heating systems, which combine air-to-water heat pumps (AWHP) with traditional gas boilers, are a common solution after refurbishment investments. However, managing these systems effectively, particularly when integrated with photovoltaic (PV) panels and battery energy storage systems (BESS), remains a complex task. For instance, heat pumps perform poorly in very cold conditions, making boilers a more efficient option; however, it might be advantageous to use it to increase electricity self-consumption. Optimal management depends on multiple factors, including future forecast data. In this paper, a daily optimization program is developed by means of a brute-force approach using forecast data. The core innovation of this paper is the use of an artificial neural network (ANN) that, trained on predictive optimization results, can determine the optimal solution in real-time without the need for future forecasts. The ANN achieved a 99.16% accuracy in new scenarios, successfully optimizing costs, CO2 emissions, and primary energy use. Results indicate up to 19% cost savings in colder cities, a 12% reduction in CO2 emissions, and a 3% decrease in primary energy consumption. This approach holds significant potential for enhancing the integration of renewable energy sources, contributing to long-term sustainability goals.

Multi-objective optimal configuration of CCHP system containing hybrid electric-hydrogen energy storage system

In order to cope with the increasing energy demand and achieve the “double carbon “goal of China’s 14th Five-Year Plan,” combined with hydrogen energy storage technology, it has the characteristics of zero pollution, high efficiency and rich source. In the context of reducing energy consumption and the vigorous development of hydrogen energy storage technology, a multi-objective optimization configuration model with economy, energy consumption index and carbon emission index is proposed, which takes into account the working characteristics of the hydrogen energy storage system, and the exothermic heat release from the electrolysis tanks and fuel cells when they are working to provide the loads with an additional heat source of the Combined Cooling, Heating and Power (CCHP) system, to reduce energy consumption and carbon emission. Finally, taking a region as an example, a multi-objective optimization algorithm based on decomposition is used to solve the model, so as to obtain a series of alternatives with better optimization effect. At the same time, the two-way projection method based on interval intuitionistic fuzzy information is used to make decisions, and the scheme that optimizes the economy, energy consumption index and carbon emission index is obtained, which verifies the feasibility of the system proposed in this paper.

Multi-Criteria Optimization of the Paper Production Process Using Numerical Taxonomy Methods: A Necessary Condition for Predicting Heat and Electricity Output in a Combined Heat and Power (CHP) System

The subject of this study is the optimization of the paper production process in one of Poland’s leading paper mills. In addition to its primary objective of paper production, the company generates heat and electricity for internal consumption and external clients, including the local municipality. Surplus energy may be sold on the power exchange; however, this requires forecasting the quantity of energy to be sold 24 h in advance, which introduces an element of uncertainty. Production stoppages, often caused by random events such as paper breakage, force a power decrease in the CHP system, further complicating energy forecasting. To minimize the occurrence of such events, numerical taxonomy methods were employed to determine the optimal screen speed (Vs) and winding speed (Vn) for two paper machines, based on the type and weight of the paper produced. This analysis utilized detailed daily data collected by the company over the period 2015–2020. The findings contribute to minimizing the occurrence of paper roll tearing, thereby reducing the risk of inaccurate forecasts of the energy and heat produced by the CHP system. Furthermore, the methodology employed in this study may be effectively applied to other optimization problems in industrial processes.

Innovative electric heating system for a hybrid solar cooker (photovoltaic/thermal) using photovoltaic energy with battery storage

Innovative electric heating system for a hybrid solar cooker (photovoltaic/thermal) using photovoltaic energy with battery storage

Experimental investigation of enhanced thermal performance of solar air collectors through optimized selection of manufacturing materials: Energy, exergy, economic, and environmental analysis

AbstractThe current study aims to experimentally investigate the thermal performance of three identical solar air collectors manufactured from three different metals: aluminum, zinc, and steel. The study conducted environmental and economic analyses of the three proposed solar air collectors over a month. The aluminum solar air collector demonstrated superior performance, achieving a maximum thermal efficiency of 24.46%, while the steel and zinc solar air collectors recorded thermal efficiencies of 21.57% and 18.16%, respectively. The exergy efficiency ranges for aluminum from 0.95% to 13.54%, zinc absorber plate exhibits from 0.63% to 11.89%, and steel from 0.6% to 9.98%. The aluminum solar air collector revealed high monthly cost savings of about $1429.6/month, followed by zinc ($1203.8) and steel ($963.4) solar air collectors. Environmental analysis to showed savings entering the atmosphere per month showed that the aluminum reduced about 19,795 kg CO2/month, Zinc solar air heat 16,668 kg CO2/month, and the steel solar air collector recorded 13,339 kg CO2/month. In this study, the absorber plate made of aluminum was the most efficient material, followed by zinc and steel. That confirms its suitability for designing efficient solar air heating systems in areas with high solar radiation intensity, such as Tunisia, and provides a clear direction for future research and development in manufacturing solar air collectors.

Optimizing Rice Heating Efficiency: A Comparative Study of Zigzag Rail Design and Hybrid Induction Heating in a Novel Rice Heating System

Optimizing Rice Heating Efficiency: A Comparative Study of Zigzag Rail Design and Hybrid Induction Heating in a Novel Rice Heating System

Comparing Feature and Trajectory-Based Remaining Useful Life Modeling of Electrical Resistance Heating Wires

Industrial heating significantly contributes to global greenhouse gas emissions, accounting for a substantial portion of annual emissions. The transition to fossil-free operations in the heating industry is closely linked to advancements in industrial electrical heating systems, especially those using resistance heating wires. In this context, Prognostics and Health Management is crucial for enhancing system reliability and sustainability through predictive maintenance strategies. The integration of machine learning technologies into Prognostics and Health Management has significantly improved the precision and applicability of Remaining Useful Life modeling. This improvement enables more accurate predictions of component lifespans, optimizes maintenance schedules, and enhances operational efficiency in industrial heating applications. These developments are essential for reducing greenhouse gas emissions in the sector. This paper serves as a guide for conducting Remaining Useful Life modeling for industrial batch processes. It evaluates and compares two methodologies: deep learning-based approaches using full time-series data, such as recurrent neural networks and their variants, and feature-engineering-based methods, including random forest regression and support vector machines. Our results show that the feature-oriented approach performs better overall in terms of predictive accuracy and computational efficiency. The study includes a detailed sensitivity analysis and hyperparameter estimation for each method, providing valuable insights into developing robust and transparent Prognostics and Health Management systems. These systems are crucial in supporting the heating industry’s move towards more sustainable and emission-free operations. The findings reveal that feature-oriented methods are both performant and robust, particularly excelling in handling outliers. The random forest regression model, in particular, demonstrated the highest performance on the test dataset according to the chosen evaluation metrics. Conversely, trajectory-oriented methods exhibited less bias across varying levels of degradation, a helpful characteristic for Prognostics and Health Management systems. While feature-oriented methods tend to systematically underestimate Remaining Useful Life at high true values and overestimate it at low actual values, this issue is less pronounced in trajectory-oriented models. Overall, these insights highlight the strengths and limitations of each approach, guiding the development of more effective and reliable predictive maintenance strategies.

ITER-relevant 600 s steady-state extraction of negative hydrogen ions at the test facility ELISE

Abstract The neutral beam heating system for the future international fusion experiment ITER will be based on RF driven ion sources delivering a large (≈1×2 m2) and homogeneous negative hydrogen or deuterium ion beam of several ten Amperes over up to several hundred seconds. The size scaling experiment ELISE (Extraction from a Large Ion Source Experiment) is an integral part of the R&D road-map towards the ITER neutral beam heating system. Recently, 90 % of the ITER target for the extracted current density was achieved in hydrogen for 600 s, increasing the pulse length over that such current densities are possible by a factor of more than ten. For ten second beam pulses the ITER target current density was achieved. These breakthrough results are made possible by a steady-state capable HV power supply together with an improved version of internal potential rods and a modified topology of the magnetic filter field.

Hybrid heating in the fused filament fabrication process

AbstractAltering the heating regime of the polymer during the fused filament fabrication (FFF) process can lead to changes in both the behaviour of the polymer and the characteristics of the printed product. This study proposes replacing the traditional resistive heating system with two hybrid systems that introduce an additional temperature of 120–160 °C: one combining resistive and hot air jet heating, and the other combining resistive and infrared radiation heating. The samples printed using these hybrid systems were analysed using differential scanning calorimetry (DSC) and visually inspected. Commercial ABS and PLA filaments were used in the experimental programme. A model to evaluate the polymer’s melting during the printing process was proposed and experimentally validated. Visual testing revealed that the printed lattice structure had smaller voids, characterised by depositions that were flattened rather than circular in cross-section due to the extended time in a viscous/partially molten state. The elongation viscosity and storage modulus decreased by approximately 10%, with a slightly smaller decrease observed for the infrared radiation heat source. The glass transition temperature remained unchanged, and the molecular mobility was not affected by the additional heat. Similarly, the energy required for crystal formation was unaffected by the supplementary heat. The mechanical behaviour of the printed pieces during compression tests was also influenced by the addition of a second heat source. For both materials, a decrease in deformability was observed as the temperature of the hot air jet increased.

Investigation of hybrid modeling and its transferability in building load prediction used for district heating systems

In the district heating systems, the historical operation data of the buildings in those areas would be partially or entirely missing. The traditional data-driven model is hard to predict the ground truth results because the historical data is not available for model training. However, utilizing the physics-based methods for load calculation takes a long time to process and encounters low accuracy issues. This paper investigates several hybrid models that integrate the data-driven model and the physics-based models with different fusion methods. The physics-based models calculate envelope load and infiltration load, based on Fourier's law and the grand canonical ensemble theory, respectively. After undergoing load processing, features fusion, and residual connection, the best advanced hybrid models generate 21.35%, 16.35%, and 12.73% better prediction results compared with the data-driven model. Moreover, the advanced hybride models also perform strong transferability across all the data quantity groups. In terms of practical application, the advanced hybrid models could be deployed with effective generalization in limited data scenarios and robust transfer capabilities. The selected best model constructed by hybrid modeling displays the highest performance and saves the total training costs with strong transferability.

Resource Efficiency and the Role of Renewable Energy in Miskolc: The City’s Journey Towards Becoming a Smart City

Miskolc, which is the focus of our investigation, is the fourth most populous city in Hungary and the center of one of the most underdeveloped NUTS2 (basic territorial category for the regional policy of the European Union) regions in the European Union. The socialist heavy industry played a decisive role in the development of the city, the decline of which also left deep traces in the city. In its current position, the city tries to manage its available resources as efficiently as possible, and the city management is open to the use of modern urban development tools. This is supported by the fact that Miskolc was the first Hungarian city to join the Green Cities for Sustainable Europe movement in 2011, and then in 2015, it joined the Triangulum project of the EU Smart Cities and Communities program as a follower city. In the process of becoming a smart city, the dimensions of environmental sustainability and energy efficiency were given a prominent role, which should not be surprising considering the traditions of the city. Within this, we must first mention the construction of the geothermal central heating system, with which the city really took significant steps in this field. The main goal of the study is to develop a new smart local concept closely linked to regional development and the key energy sector, through which the local adaptation of the defining elements of the internationally defined smart city in several forms for the city of Miskolc will be presented. In our study, we review how the results achieved by Miskolc so far and the development plans for the future fit in with the smart energy developments of smart cities. Before exploring the processes in Miskolc, we will deal in more detail with the possibilities inherent in district heating and geothermal energy utilization and Hungary’s capabilities.

Research on Hybrid Heating System in Cold Oilfield Regions

Efficient and clean treatment of wastewater and energy recovery and utilization are important links to realize low-carbon development of oilfields. Therefore, this paper innovatively proposes a multi-energy complementary co-production heating system which fully and efficiently utilizes solar energy resources, oilfield waste heat resources, and biomass resources. At the same time, a typical dormitory building in the oil region was selected as the research object, the system equipment selection was calculated according to the relevant design specifications. On this basis, the simulation system model is established, and the evaluation index and operation control strategy suitable for the system are proposed. The energy utilization rate of the system and the economic, energy-saving, and environmental benefits of the system are simulated. The results show that, under the simulated conditions of two typical days and a heating season, the main heat load of the system is borne by the sewage source heat pump, the energy efficiency is relatively low in the cold period, and the energy-saving characteristics are not obvious. With the increase in heating temperature and anaerobic reactor volume, the energy consumption of the system also increases, and the energy efficiency ratio of each subsystem and the comprehensive energy efficiency ratio of the system gradually decrease. In addition, although the initial investment in cogeneration heating systems is high, the operating costs and environmental benefits are huge. Under the condition of maintaining 35 °C, the anaerobic reactor in the system can reduce carbon emissions by 12.15 t per year, reduce sulfur dioxide emissions by 98.4 kg, reduce dust emissions by 49.2 kg, and treat up to 2700 t of sewage per year, which has broad application prospects.

Carbon monoxide poisoning: assessment and actions for nurses working in service users' homes.

Carbon monoxide is a colourless, tasteless and odourless gas that is a by-product of incomplete combustion of hydrocarbons such as gas or coal. Carbon monoxide poisoning is associated with high levels of mortality and morbidity, although the symptoms of exposure are non-specific and difficult to diagnose. Historically, healthcare professionals who work directly in service users' homes have faced a higher risk of exposure to environments contaminated with carbon monoxide. Despite improved heating engineering and greater protective legislation, there still remains a tangible risk for community nurses and service users, particularly in homes still using open fires, old heating systems and poorly maintained heating appliances. This article provides a scientific summary of carbon monoxide poisoning for nurses working in the community setting. It outlines evidence-based guidance on prevention and initial treatment actions, along with safety instructions to follow in the event of exposure to carbon monoxide.

A model predictive control strategy of global optimal dispatch for a combined solar and air source heat pump heating system

Due to low-carbon transformation of heating sources in northern China, the combination of the solar heating system (SHS) and the air source heat pump (ASHP) has attracted widespread attention due to their significant low-carbon and energy-saving characteristics. Nevertheless, different outdoor meteorological parameters simultaneously affect both the SHS and the ASHP. The heat supply of the SHS is primarily influenced by solar radiation intensity, and the efficiency of the ASHP is mainly constrained by outdoor temperature. The dual uncertainty and volatility of the output capacities of these two heat sources pose significant challenges to their coordinated control. Previous research has primarily focused on traditional rule-based control (RBC) and feedback control, prioritizing the utilization of solar energy while employing ASHP to compensate for heating deficiencies. However, these methods ignore the variations in ASHP efficiency due to fluctuations in outdoor temperature, leading to low whole-system operating efficiency and limiting the flexibility and responsiveness of the control. In this paper, a model predictive control strategy of global optimal dispatch for a combined solar and ASHP (SASHP) heating system is proposed, which focuses on the flexibility and adaptability of dual heat source dispatch under different external conditions. A Temporal Convolutional Network (TCN) was used to establish a solar radiation intensity and load prediction model. A solar heat production prediction was realized by combining the mechanism model based on solar radiation intensity prediction; a two-step room temperature prediction model was established by introducing new input parameters in the load prediction. This strategy achieves globally optimal dynamic planning of the heat supply and duration of the SHS and ASHP systems by considering solar radiation, outdoor temperature, room temperature, and energy consumption, ensuring the efficient and stable operation of the system. Compared to RBC, experimental results indicated that under conditions of sufficient solar energy, the heating proportion of the SHS increased by 31.1 %, the average COP of the ASHP improved by 8.7 %, the energy-saving rate was 14.6 %, and the room temperature control was also more effective; whole-season simulation results showed an average energy-saving rate of 8.35 %.

Comprehensive energy footprint of electrified fleets: School bus fleet case study

This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel under the average price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.

Performance response analysis and optimization for integrated renewable energy systems using biomass and heat pumps: a multi-objective approach

The focus of this study is to optimize the exploration of biomass-driven multi-energy systems, which include combined heat, power, and gas generation. The objective is to enhance the thermal, environmental, and economic performance indicators of the system. The optimization objectives encompass the quantities of internal combustion engines and air source heat pumps, as well as the dimensions of tanks utilized for anaerobic fermentation. A mathematical model was developed to optimize multiple objectives for combined heat, power, and gas generation systems by employing multi-objective intelligent optimization algorithms. The validation and analysis were conducted using rural residences in Lanzhou, Gansu Province, China, as a case study. The sensitivity analysis of biomass gasification combined heat and power systems was conducted from both technical and cost perspectives, examining the dynamic impact characteristics on the outcomes of multi-objective optimization. The findings indicate that the annual energy-saving rate of the optimized combined generation system decreased from 3.62% to -6.78%, while the growth in carbon emissions reduction rate increased from 76.05 to 81.38%, and the annual cost-saving rate grew from 0.97 to 14.96%. The power generation efficiency of the cogeneration station and hydraulic retention time were found to have a significant impact on the multi-objective optimization results of the combined generation system among the technical parameters. The unit cost of anaerobic fermentation tanks had a more significant impact on the multi-objective optimization results in terms of cost parameters, compared to the price of biogas residue.

A molten salt energy storage integrated with combined heat and power system: scheme design and performance analysis

To investigate the flexibility and economic characteristics of a molten salt-combined heat and power (CHP) integrated system under different heat sources, this paper proposes a design scheme for a molten salt-CHP system based on flue gas heat storage, comparing it with main steam heat storage and reheated steam heat source schemes. First, a molten salt heat release sub-loop is designed, where the steam heated by the molten salt can either compensate for heating demands or enter the low-pressure turbine for work, meeting the flexible operation requirements of the unit. Secondly, in response to fluctuations in temperature and pressure parameters of the main steam and reheated steam in the flue gas heat storage scheme, a water-spray desuperheating device is arranged. Finally, a simulation experiment based on a 350 MW CHP was conducted, and the results show that the flue gas molten salt heat storage technology significantly reduces energy loss on the boiler side. Underrated load, compared to the main steam and reheated steam heat storage schemes, the flue gas heat storage scheme has the highest boiler exergy efficiency and total energy utilization efficiency, at 55.8% and 59.1% respectively. During the thermal storage process, the coal consumption index of the flue gas heat storage scheme decreases with increasing load, while conversely, during the heat release process, it increases with the load. The peak-shaving capacity increases with the load, reaching 78.4 MWh under the 75% THA condition. In the steam extraction heating scenario, coupling with the molten salt subsystem can expand the safe heating operation range of the original unit, increasing the maximum peak shaving range by 13.9%, and increasing the maximum heating capacity by 65.4%.