Thermal Demand Research Articles

Economic dispatch of a bioclimatic office building considering thermal energy, electricity and water demands

Energy and otherresourcesmanagement in produc-tionsystems is a common topicin recent literatureas many envi-ronmental and economic issues are related to the efficient use of theirdevices. In this sense,conversion and storage models based oninput-outputbalances are helpful todetermine the combination of resources that allow tooperate a plantwith the lowest possible cost. This paperis aimed at analyzingthe optimal flows of re-sourcesthat would meet the demands of abioclimatic building,taking into accountthe characteristics of itsfacilities, which in-cludes both photovoltaic modules and solar collectors to produce self-consumption electric and thermal energy.The simulation re-sults for two different days of 2018have been included. They ver-ifythe validity of the proposed model, which could be extended for carrying out analyses on similar buildings, modelled as an en-ergy hub, in order to assess its optimality and proposing manage-ment policies.

Numerical investigation on thermal performance of phase change materials embedded in functionally graded metal foam

Latent heat thermal energy storage (LHTES) technology based on phase change materials (PCMs) embedded in metal foam has recently become a popular method to store thermal energy and release it to other processes with thermal energy demand, which has been applied in solar energy, electronic devices, waste heat recovery, etc. In this study, we proposed a novel design of metal foam with porosity changing over the horizontal and vertical coordinates. The thermal resistance model method was applied to explore the heat transfer mechanism and linear porosity was proved to be able to improve the convection intensity in different stages of melting. Also, metal foam with horizontal linear porosity or vertical linear porosity can both shorten the total melting time and increase the average power density, while composite linear porosity can combine the enhancing effect of these two structures and achieve an optimal enhancing effect. The optimal linear porosity gradient can enhance the average power density by 15.8 % compared to those of uniform porosity. Furthermore, the heat storage performance of linear porosity with different enclosure aspect ratios was also discussed. The linear porosity structure was proved to be able to balance the nonuniformity of melting caused by natural convection.

Optimal energy management of energy hub: A reinforcement learning approach

Increasing energy demand in today’s world emphasizes the importance of optimal scheduling for distributed energy resources to minimize energy costs and greenhouse gas (GHG) emissions. The efficiency of this decision-making process relies on accurate modeling. In this paper, reinforcement learning (RL), an artificial intelligence-based approach, is proposed to optimize the energy management system (EMS) of an energy hub (EH). This EH contains renewable energy resources (RER), a combined heat and power (CHP), and a gas furnace. In order to meet electrical and thermal energy demand, available options such as day-ahead and real-time purchases from the main grid, RERs, and natural gas consumption are managed, with the preference of RERs to minimize GHG emissions and energy costs. With the adaptable RL method, a non-linear model of the CHP operation is constructed, considering the operational costs of the CHP. Furthermore, the natural gas tariff is varied according to the consumption level of the microgrid. Finally, this paper presents an RL-based method for EMS optimization of an EH with day-ahead and real-time scheduling, applied to a 24-hour case study with linear and nonlinear modeling of the problem and sensitivity analysis of the parameters. Corresponding simulation results show the efficiency of the presented approach.

Micro-patterned glass for energy-efficient windows

Development of novel energy-efficient components is a topic of prime importance in building design. In this contribution, we propose a micro-patterned glass window which can be designed to efficiently reduce the energy demands of buildings located within an area of hot and cold seasons. The proposed patterned structure can be applied to any panes of glass in single or multi-pane windows. We first define the total positive power as a characteristic parameter for evaluating the effectiveness of using the designed window in reducing the average thermal power demands of a given building. Then, a full-wave numerical method is employed to analyze the performance of the proposed structure with respect to the defined characteristics. Our model accurately accounts for sunlight incident angle variations corresponding to different times of the days in every season. In addition, the variations of glass refractive index throughout the solar spectrum, non-polarized nature of the sun light, and typical dimension scales used in building windows are taken into account. Our numerical results show more than 80 W and 160 W total positive power for an optimized single-pane micro-patterned window with unit area and a double-pane one, respectively. We also present the design procedure of the proposed structure for a given geographical location of the building.

FORECASTING THERMAL ENERGY DEMANDS FOR VARIOUS PROCESS INDUSTRIES USING MACHINE LEARNING TECHNIQUES

Despite the fact that India has vast solar energy potential, the process industries are dependent on conventional fossil fuels for their thermal energy needs. Solar thermal energy is a viable option for industrial process heating applications to mitigate the utilization of conventional fossil fuels. The utilization of solar energy in the process industries to meet their energy demands helps to reduce the carbon footprint, and eventually will help India balance its energy needs by reducing the import of crude oil. The exploitation of solar energy in the process industries requires the assessment of future thermal energy demands. In this study, since the data points were nonlinear in nature, support vector regression (SVR) and long short-term memory (LSTM) algorithms were applied to forecast the thermal energy demands of various process industries. The process industries considered in this study were textile, food processing, leather and footwear, chemical and pharmaceutical, dairy, iron and steel, and automotive. Data from 1998 to 2014 were used for training and data from 2015 to 2017 were used for testing. The mean absolute percentage error (MAPE) was used as a performance measure metric to measure the performance of the SVR and LSTM algorithms. The average MAPEs obtained for given industries by the SVR with the genetic algorithm (SVRGA), SVR with grid search (SVRGS), and LSTM algorithm were 7.56%, 8.34%, and 11.10% respectively. The SVRGA outperformed the SVRGS and LSTM algorithm for the given training and testing data.

Numerical analysis, design and application of a decentralized ventilation system with a heat recovery unit adapting to Nordic climates: A case study in Norway

This research investigated the energy efficiency of decentralized ventilation (DV) systems in cold weather conditions in comparison to centralized ventilation (CV) systems, focusing on three locations in Norway. The study found that DV systems with higher heat recovery unit (HRU) efficiency consumed less thermal and fan energy due to shorter air distribution pathways, utilizing a hybrid natural ventilation strategy in mild climates, and simpler space zoning for ventilation. This study proposes to determine a suitable ventilation system for adapting to cold climate. These methodologies are presented in three major parts: 1) ventilation energy demands of DV and CV systems in Norway, 2) performance of the heat recovery unit in the DV system, and 3) numerical analysis of the total ventilation energy demand of the DV system in cold climates using occupant ratio and existing measured data.The results showed specific time periods when it is ideal to use a fan-assisted NV system in cold climates, and in local conditions, 4–20% per year could benefit from a fan-assisted NV system using DV technology. A DV system with 0.7 HRU efficiency can save up to 14.5% of total HVAC energy compared to a CV system. The HRU heating efficiency played a crucial role in thermal ventilation energy demands in cold weather, while the cooling efficiency had no significant effect on energy demands. However, if a hydronic heating and cooling coil is added to the DV system, it consumes more than 9.08% of energy than using a pump. The energy-saving performance of the DV system decreases significantly if its HRU efficiency is below 50%, and there is no real advantage of using the DV system over the CV system in such cases. Nonetheless, the DV system can be utilized in various working conditions to conserve energy, such as different occupancy ratios, individual room space zoning, and indoor air pollution rates.

Influence of different heating systems on thermal comfort perception: a dynamic and CFD analysis

In this paper, we investigate the influence of different heating systems on the thermal comfort indexes, Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), for a residential apartment located in Bologna (Italy). The apartment has an area of 40 m2 and is located on the ground floor of 4 floors building. The envelop consists in horizontal perforated bricks with internal thermal insulation material and two windows. The analyses are performed employing Trnsys, a commercial dynamic simulation software and Simcenter STAR-CCM+, a multiphysics computational fluid dynamics (CFD) software. The CFD analysis regards a steady condition of a typical winter day in Bologna. Thermal comfort indexes and thermal energy demand are studied comparing two different heating generation systems existing in the considered apartment: a condensing gas boiler coupled with radiators as terminal emitters and an air-to-air heat pump. By crossing the results obtained by the dynamical approach and by the CFD simulations, a two-objective methodology where energy consumption is minimised while thermal comfort is obtained, is presented.

Optimal Scheduling of Micro-Energy Grid Based on Pareto Frontier Under Uncertainty and Pollutant Emissions

With the continuous improvement of micro-energy grid, in order to reduce carbon emissions and environmental pollution, a multi-objective optimization operation strategy considering the distributed energy generation uncertainty, environmental factors, and self-supply rate is proposed in this paper. First, to simulate the uncertainty of distributed energy, we used the three-parameter Weibull distribution to simulate the wind power. Then, for the load side, an innovative electric and thermal comprehensive demand response model is proposed. Different from the traditional micro energy network that only considers the heat energy balance, this paper also considers the thermal inertia of buildings to more accurately describe the characteristic of the heat load. Meanwhile, on the basis of economic factors, the abandon rate of distributed energy, environmental coefficient and self-sufficiency rate are also regarded as three objective functions. Finally, in order to obtaining a set of solutions at the Pareto front, the entropy-weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is introduced to obtain the global optimal solution. Simulation study of multiple scenarios shows the effectiveness of the proposed method. Compared with the method that considers only economic costs, the pollutant emissions are reduced by 50kg and self-power supply rate is increased by 20%.

Insights of water-to-hydrogen conversion from thermodynamics

<p>Water-to-hydrogen can be achieved using a variety of driving energy sources, including thermal, electrical, or photo energy. While methods for hydrogen production in specific energy driving scenarios have been extensively studied, a comprehensive theory to explain the conversion of various energies into hydrogen is still lacking. This study provides a novel exergy-based perspective on hydrogen production methods, revealing that the thermodynamic infeasible water splitting process is derived from insufficient exergy input relative to the reaction exergy requirement. Enhancing the exergy input beyond the reaction exergy requirement can break through chemical equilibrium and enable the reaction to proceed. Providing high exergy-to-energy ratios of energy sources such as electrical, photo, and chemical energy for thermochemical water splitting reactions can reduce the thermal exergy demand for hydrogen production, thus facilitating water-to-hydrogen conversion at lower temperatures. By applying this new insight to coupled photochemical- and thermochemical water splitting reactions, equilibrium conversion rates corresponding to solar spectra with different wavelengths are obtained. The highest water-to-hydrogen conversion rate is achieved by the solar spectrum at a wavelength of about 451nm. The appropriate wavelength region for high water-to-hydrogen conversion is identified. This study also identifies the theoretical conversion limit of photochemical water splitting, providing insights into the potential improvements of current experiments. More importantly, our work offers a unified thermodynamic framework for understanding hydrogen production methods and presents a theoretical basis for reducing reaction temperature and enhancing conversion rate.</p>

Performance assessment of a system to provide steady high temperature air via solar thermal suspension flow technology with storage and combustion back-up

The imperative for economically viable sources of high-temperature heat without contributing to net carbon emissions has become a pressing global concern, particularly in the context of industrial chemical processes. Solar thermal heat generation emerges as a pivotal solution in addressing this formidable challenge. By concentrating sunlight to achieve high temperatures, solar thermal technology can provide the intense heat required for various industrial applications, including chemical processes, without relying on fossil fuels. This not only helps reduce greenhouse gas emissions but also enhances energy efficiency and sustainability in energy-intensive industries. However, the full integration of solar thermal systems into energy-intensive industrial processes necessitates continued advancements of new technologies to achieve temperatures in the order of 1000 °C. In this paper, the performance of a high temperature concentrating solar thermal plant based on a 50MWth suspension flow solar particle receiver sub-system integrated with sensible thermal storage and combustion backup is analyzed, to supply steady output of reheated air to a downstream thermochemical process. The analysis is performed with transient mathematical models of the receiver and packed bed thermal storage sub-systems considering solar resource variability, written in a Simulink environment, to estimate useful annual thermal gain, thermal efficiency, solar share and levelized cost of solar heat, for a range of parameters including particle mass loading, air mass flow rate, thermal storage capacity and solar multiple. The time-dependent temperature fields of the receiver and thermal losses, together with their influence on the performance of the combined system are reported for each condition with a transient input solar resource spanning over a whole year. New insights are provided of the relative tradeoffs between these parameters on the overall system performance, solar share and levelized cost of solar energy. Also reported is the sensitivity of the levelized cost to different system combinations sized to provide a solar share of up to 75 % of the yearly process thermal demand, along with the influence of the specific costs of the system components varied in the range of ± 60 %.

Divergent gametic thermal performance and floral warming across an elevation gradient.

Thermal environments vary widely across species ranges, establishing the potential for local adaptation of thermal performance optima and tolerance. In the absence of local adaptation, selection should favor mechanisms to meet thermal optima. Floral temperature is a major determinant of reproductive success in angiosperms, yet whether gametic thermal performance shows signatures of local adaptation across temperature gradients, and how variation in gametic thermal performance influences floral evolution, is unknown. We characterized flowering season temperatures for the forb, Argentina anserina, at extremes of a 1000 m elevation gradient and generated thermal performance curves for pollen and ovule performance in populations at each extreme. Thermal optima fell between mean and maximum intrafloral temperatures. However, cooler high-elevation populations had ~4 °C greater pollen thermal optima than warmer low-elevation populations, while tolerance breadths did not differ. We then tested whether plants at elevational extremes differentially warmed the floral microenvironment. High-elevation flowers warmed significantly more than low, bringing intrafloral temperatures nearer the pollen optima. A manipulative experiment demonstrated that stronger warming in high elevation was conferred by floral tissues. Elevational divergence in floral warming may be driven, in part, by selection on flowers to meet different thermal demands of the gametophytes.

Design and Performance Analysis of an Air-Based Photovoltaic/Thermal Collector in Winter

The most basic needs of the buildings in which we spend almost all of our time are electricity and heat demands. Especially in cold climatic regions, it is very important that both electrical energy and heating energy demand are met through the same system. The use of photovoltaic (PV), is increasing rapidly. Photovoltaic panels can transform solar energy into electrical energy with less than 20% performance. Photovoltaic-thermal(PV/T) collectors which can be mounted on facades of buildings or used as building envelopes, are one of the solar energy applications. With this collector, both electric energy and thermal energy demand is produced from solar energy. In our work, Photovoltaic panel and some kind of solar air collector have been taken into consideration. In order to determine the behavior of the air-based photovoltaic-thermal collector, an experimental setup and a measurement system have been established. The experimental setup consists of this measurement system, sensors that obtain data, and a data storage unit that can transmit temperature, humidity, and radiation data to the computer at the desired frequency. In order to determine the performance of the air-based PV/T collector, the efficiencies of both the PV and the solar air collector were calculated separately. To determine the performance of this collector, calculation criteria’s and model has been determined. When creating this model, the problem has been considered as time-dependent under irregular conditions. The theoretical analysis model, which was established to determine the performance of the air-based PV/T collector, was evaluated according to the winter climatic conditions of Izmir-Turkey.

Integrated climate-responsive thermal load ML model and cost/embodied energy estimate from a preliminary building design

This paper proposes a framework applicable to preliminary building design that cohesively integrates material cost estimation, embodied energy assessment, and the prediction of the thermal demand of a building. At its core is a climate-responsive machine learning model specifically developed using readily accessible early-stage design data. This model forecasts the thermal demand both in the present and in the face of likely climatic shifts. To strike a balance in energy performance goals, material cost estimation is seamlessly incorporated. Building on the gradient boosting machine, the proposed approach showed better performance than other algorithms, predicting thermal demand 25 to 37.5 times faster than conventional energy software without compromising accuracy. The key findings of the presented case studies highlight the significant influence of the Window-to-Wall ratio and floor thermal mass on thermal demand—especially in mitigating cooling demands posed by potential climate change scenarios. By enhancing interior floor types, heating demand can be reduced by nearly 49%. The study further suggests that incorporating climate change considerations into life cycle energy designs is projected to amplify thermal and cooling loads by 32% and 94%, respectively, highlighting the crucial role of early design decisions in shaping sustainable and efficient buildings.

Thermal performance analysis and thermal environment creates effect of a novel solar hot-air phase change bed

Comprehensive heating provides a comfortable indoor but consumes significant energy. Temporal-spatial partitioned heating can meet differentiated thermal demands while reducing energy consumption. A novel solar hot-air phase change bed heating system has been proposed for achieving temporal-spatial partitioned heating. Theoretical analysis models have been established to study the heat transfer process of the hot-air phase change heating bed, and numerical simulations have been conducted to analyze its thermal performance and creation of a comfortable environment. The results demonstrate that at the end of the heat storage, the bed board temperature increased by 1.5 °C for every 5 °C increase in the wind temperature. The maximum temperature difference of the bed board reached 6.1 °C under three different wind speed schemes. During heat release, the bed board heat flux was affected by the phase change material heat storage difference, and the maximum temperature difference of the bed board reached 4.3 °C ∼ 6.7 °C under different schemes. After the first cycle, both indoor and bed board temperature were higher than the initial values. During the daytime, there was excessive heat indoors, and the maximum environmental temperature exceeded the comfortable range by 8.7 °C. At night, the bed was covered with a high-temperature layer, and the bed surface temperature was uniformly distributed, remaining in the thermal comfort zone for 3.8 h to 5.1 h. The research results provide a basis and guidance for utilizing solar hot-air phase change beds to improve differentiated thermal demands.

Comparative summer thermal performance analysis between open ventilated facade and modular living wall

In recent years, passive solutions for building envelopes have become much more common due to their capacity to decrease the heat flux through the envelope during summer time. Vertical greenery systems (VGS) are emerging as an interesting method of decreasing the thermal demand of cities, and also improving the quality of urban life. Open ventilated facades (OVF) have gained popularity due to their capacity to enhance the thermal resistance of the building envelope. As part of a project carried out in a Paslink cell in Vitoria-Gasteiz, an experimental campaign with full-scale VGS and OVF was carried out during the summer season to assess the thermal performance of a modular living wall (MLW) with respect to an OVF. The objective is to demonstrate that a stochastic differential equations (SDE) model can be used to assess the cooling requirements of an MLW and an OVF. An analysis was carried out to evaluate how different characteristics of the main facade affect performance, such as thermal resistance, solar absorption coefficient and convection coefficient. The results of these experiments show that both MLW (46 %) and OVF (67 %) configurations significantly minimize solar heat loads compared to non-passive bare wall (BW) facades, which are the reference configurations.

Performance study of a thermochemical energy storage reactor embedded with a microchannel tube heat exchanger for water heating

Thermochemical energy storage (TCES) provides a promising solution to addressing the mismatch between solar thermal production and heating demands in buildings. However, existing air-based open TCES systems face practical challenges in integrating with central water heating systems and controlling the supply temperature. To overcome these limitations, a novel water-based TCES-HEX-HRU system is proposed in this study, which integrates a water-to-air microchannel tube heat exchanger (HEX) and an air-to-air heat recovery unit (HRU). A comprehensive evaluation of the TCES-HEX-HRU system is conducted numerically using a COMSOL model, including a comparative assessment for different TCES system configurations. The results demonstrate that the TCES-HEX-HRU system achieves an overall thermal efficiency of 82.35 %, marking a substantial 69 percentage points improvement over the TCES-HEX system. Although slightly lower than the typical air-based TCES system without HEX and HRU by 15.44 percentage points, the TCES-HEX-HRU system can be a practically promising and viable choice for applications in central heating systems. Numerical investigations indicate that the thermal performance of the system is influenced by the inlet conditions of airflow and waterflow. Moreover, increasing the number of water channels in the HEX of the TCES-HEX-HRU system enhances heat transfer but reduces the amount of heat released by TCES composite materials, resulting in a maximum overall thermal efficiency of 92.09 % with 35 channels and a peak outlet water temperature of 33.67 °C at with 30 channels. However, further increases in the number of channels lead to a decline in overall thermal efficiency and outlet water temperature. Changes in the width of water channels in the HEX have a minor impact on the highest outlet water temperature and overall thermal efficiency, while affecting the volume of the TCES composite materials.

Continuum damage mechanics of cyclic viscoplasticity using asymptotic numerical method

Conscious efforts on reduction of greenhouse gas emissions have led to an energy transition to renewable energy, however uncertainties of renewable energy production have resulted in higher thermal cycling demands from conventional power plants. Thermal load cycling at high temperature regions of steam turbine components leads to enhanced creep-fatigue damage accumulation. It is well established that such damage mechanism is numerically best predicted by unified constitutive modeling including damage as a variable as per the formalism of continuum damage mechanics at an expense of considerable computational efforts using finite element analysis. In this paper, the non-iterative Asymptotic Numerical Method (ANM), currently limited to partial cycle analysis with linear hardening plasticity model, is proposed for the first time to address cyclic viscoplasticity problems including damage capable of handling multiple cycles and most generalized loading conditions. Regularization techniques additionally necessary to implement loading-unloading-reloading criteria and advanced constitutive models etc. are presented. The constitutive model chosen for the formulation includes the non-linear multiple back stress variable modified Chaboche model to include damage combined with modified Chaboche-Rousselier isotropic hardening model to include damage, power law for viscoplasticity, Lemaitre’s damage potential and Kachanov-Rabotnov’s creep damage law. The method is verified with defined error measures and then applied to two high pressure steam turbine rotors, one with and another without thermal stress relief groove (TSRG) at the inlet under service type loading conditions to study the beneficial effect of the TSRG on creep-fatigue damage evolution. The accumulated errors of the proposed ANM and computational time are compared to a conventional Newton-Raphson solution.

Regression-based sensitivity analysis and multi-objective optimisation of energy performance and thermal comfort: Building envelope design in hot arid urban context

Previous research has established that energy demand of buildings in street canyons are affected by the surrounding urban physical characteristics. This suggests that it is possible to optimise the energy performance of buildings by acting on the physical urban parameters that potentially influence their overall energy consumption. Unfortunately, few studies have addressed this issue. Precisely, improving buildings thermal behaviour and energy demand pattern throughout sensitivity-based optimisation by focusing on the building’s envelope design without neglecting the effect of urban context, remains a relatively under-explored research topic. The present study attempts to fill this gap by implementing a combined method based on sensitivity analysis and multi-objective optimisation of energy demand and thermal comfort for an existing residential building subjected to hot arid climatic conditions. The proposed method utilises Latin hypercube sampling with multivariate multiple linear regression to identify the relative importance of design parameters on building performance criteria, namely, the annual energy demand and the integrated summer thermal discomfort. Non-dominated sorting genetic algorithm II (NSGA-II) is then applied to simultaneously optimise the investigated building performance criteria with regards to the identified relevant envelope parameters. The results of sensitivity analysis indicate that window characteristics, thermal insulation, and roof reflectance are the most influential factors. The optimisation results, in turn, show promising improvement potential with a wide range of design solutions that can ensure better energy performance as well as better thermal comfort. The annual energy demand could be significantly improved by 13.2% up to 49.5% while discomfort degrees could be decreased by over 26%.

OPTIMIZING ENERGY HUB SYSTEM OPERATION WITH ELECTRICAL AND THERMAL DEMAND RESPONSE PROGRAMS

Electricity consumption is increasing rapidly and many countries are looking for ways to cope with the energy crisis. Morever, the world is facing the problem of global warming caused by emissions. Therefore, it is of great importance to operate power systems efficiently. Energy Hub (EH) represents a versatile energy system capable of providing efficient and optimal solutions for the operation of power systems across multiple carriers. This paper examines the optimization of an EH encompassing renewable energy systems (RES) like wind and photovoltaic, combined heat and power (CHP), transformer, absorption chiller, energy storage system (ESS) and furnace with aiming at minimizing the cost. Demand response is an energy sector strategy that entails modifying electricity consumption patterns in reaction to fluctuations in electricity supply or pricing. The objective of demand response programs (DRP) is to curtail or shift electricity consumption during periods of elevated electricity prices. Therefore, Electrical Demand Response Program (EDRP) for electrical demand and the Thermal Demand Response Program (TDRP) for heating demand are incorporated into the EH. The optimization problem is formulated as Mixed Integer Linear Programming (MILP) and solved with CPLEX solver in GAMS. The outcomes of various case studies are compared to ascertain the model's efficiency.

Exploring a novel tubular-type modular reactor for solar-driven thermochemical energy storage

Thermochemical energy storage (TCES) has gained extensive attention as a potential solution to address the mismatch between solar thermal energy production and demand. In this study, a novel tubular-type modular TCES reactor is introduced. COMSOL modelling of the system is developed and experimentally validated using a laboratory-scale TCES system. Both types of reactors show similar temperature increases, intensifying with higher inlet relative humidity. Their maximum temperature lifts exceeding 26 °C at 90 % RH. Tubular designs offer better axial flexural strength and dispersion of TCES composite materials compared to plate structures. This property of tubular structures beneficial reducing bed thickness and pressure drop and enhancing equivalent thermal efficiency. Simulations show tubular-type modular reactors reduce pressure drop by 4–5 times compared to plate-type modular reactors, increasing equivalent thermal efficiency by nearly 7% points. Increasing the number of reactor beds and inner tube radius improves equivalent thermal efficiency due to reduced bed thickness and pressure drop. As the number of matrix rows and columns in the reactor bed increases from 2 to 10, bed thickness decreases from 0.058 m to 0.012 m, reducing pressure drop from 845.53 Pa to 38 Pa and increasing equivalent thermal efficiency from 78.82 % to 96.61 %.