Energy Model Research Articles

Pseudo Master Curve Analysis of an Infinite Number of Parallel First-Order Reactions: Improved Distributed Activation Energy Model.

The so-called Distributed Activation Energy Model (DAEM) has been used extensively, mainly to analyze pyrolysis reactions of solid reactants. The model expresses many parallel first-order reactions using the distributions of activation energy f(E) and frequency factor k 0(E). Miura and Maki presented a method to estimate both f(E) and k 0(E) in the DAEM in 1998. This model has been used successfully by many researchers. In this paper more general basic equations are derived for describing an infinite number of parallel first-order reactions by extending the basic equations for the finite number of parallel first-order reactions. Revisiting the Miura-Maki method based on the general basic equations, a graphical analysis method that may be called "Pseudo Master Curve Analysis" is presented. The method not only supplements the Miura-Maki method but gives the underlying concept of the Miura-Maki method clearly. It is also shown that the graphical method can be applicable to analyze single reactions and the experimental data obtained using isothermal reaction techniques. Next, a method that improves the estimation accuracy of k 0(E) is presented. Practical examples analyzing several experimental data are also given to show the usefulness and validity of the Miura-Maki method and the graphical method. Through the examination, it is proposed that the DAEM should be renamed, for example, as the Distributed Rate Constant Model (DRCM).

Research on Rock Energy Constitutive Model Based on Functional Principle

The essence of rock fracture can be broadly categorized into four processes: energy input, energy accumulation, energy dissipation, and energy release. From the perspective of energy consumption, the failure of rock materials must be accompanied by energy dissipation. Dissipated energy serves as the internal driving force behind rock damage and progressive failure. Given that the process of rock loading and deformation involves energy accumulation and dissipation, the rock constitutive model theory is expanded by incorporating energy principles. By introducing the dynamic energy correction coefficient, according to the law of the conservation of energy, the total energy exerted by external loads on rocks is equal to the energy dissipated through the dynamic energy inside the rocks. A new type of energy constitutive model is established through the functional principle and momentum principle. To validate the model’s accuracy, a triaxial compression test was conducted on sandstone to examine the stress–strain behavior of the rock during the failure process. A sensitivity analysis of the parameters introduced into the model was conducted by comparing the model results, which helped to clarify the innate laws of significance of these parameters. The results indicated that the energy model more accurately captures the non-linear mechanical behavior of sandstone under high-stress loading conditions. The model curve fits the test data to a high degree. The fitting curve was basically consistent with the changing trend of the test curve, and the correlation coefficients were all above 0.90. Compared with other models, the model based on the energy principle not only accurately reflects the rock’s stress–strain curve, but also reflects the energy change law of rock. This has reference value for the safety analysis of rock mass engineering under loading conditions and aids in the development of anchoring and support schemes. The research results can fill in the blanks that exist in the energy method in terms of rock deformation and failure and provide a theoretical basis for deep rock engineering. Moreover, this research can further improve and extend the rock mechanics research system based on energy.

Model-based reinforcement learning approach for federated learning resource allocation and parameter optimization

In this paper, we investigate the performance of a model-based approach for solving resource allocation and parameter adjustment problems in federated learning (FL) within a wireless network. Given the existence of models for energy, communication channels, and accuracy, such models can be leveraged to achieve improved performance. Additionally, machine learning techniques can be employed to identify known parts of the model and also exploit training data for unknown parts of the model, enabling the creation of complex policies. Model-based reinforcement learning (RL) methods have the potential to offer such solutions, particularly in resource allocation and parameter optimization settings where the model can be partially derived mathematically. Our results demonstrate that the use of such a method in FL scenarios leads to improvements in both performance and the number of iterations required to identify the desired policy. Our simulations demonstrate the significance of allocating appropriate resources for FL applications through proper consideration of inherent tradeoffs, as performance will not improve beyond a certain saturation point. Additionally, our proposed FL model takes intelligently into account the presence of slow users to propose efficient policies for users that may have access to more abundant resources.

Navigating the transition: Modelling the path for net-zero European building sector

Transition towards a net-zero building sector offers substantial potential to mitigate climate change impacts and thus plays a pivotal role in meeting the goals set forth in the Paris Agreement. Stakeholders often use mathematical models to understand the true potential for mitigating emissions by reducing the energy demand of the building sector. However, the existing models rarely provide insights into net-zero pathways for both the residential and tertiary buildings as this is a complex sector to calculate the future pathways towards net-zero. Hence, uncertainties in the associated policy decisions persist. Therefore, this study uses the High-Efficiency Building (HEB) energy model to calculate the energy demand reduction potential as well as to analyze the net-zero feasibility of the building sector for each of the European Union Member States. This research uses four scenarios, which are designed to represent scenario-specific levels of commitment the European Union Member States regarding the application of different energy efficiency measures, to provide insights into the consequences of today's policy decisions by 2060. The findings of this study show that energy demand of the building sector can be reduced substantially and can even become nearly net-zero by 2060. Based on the demand reduction potential of the building sector, it can be concluded that the building renovation rate and construction of new advanced buildings not only provides unprecedented potential for reducing energy demand but also paves the way for reaching climate neutrality goals.

Association between vitamin B2 intake and cognitive performance among older adults: a cross-sectional study from NHANES

The impact of vitamin B2 (riboflavin) intake on cognitive performance among older adults in the United States (US) remains inadequately understood. This study aimed to explore the association between vitamin B2 intake and cognitive performance among non-institutionalized elderly people in the US. Weighted logistic regression was used to evaluate the association between vitamin B2 intake and cognitive performance. Vitamin B2 intake was determined from the mean of two 24-hour dietary recall interviews. Three cognitive ability assessment tests, namely the Immediate Recall Test (IRT), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST), were performed. Participants included all older adults over 60 who underwent cognitive scoring, with cut-offs defined based on the lowest quartile (25th percentile) for each test (the cut-offs for the three scores were 15.625, 12, and 33, respectively). Sensitivity analysis, including dose-response curves, subgroup analyses, interaction effects, per 1 standard deviation (SD), recommended dietary allowance (RDA), and residual energy model analysis, were performed to solidify the solid association between vitamin B2 and cognitive performance. A total of 2893 individuals aged over 60 were included, with a mean age of 69 (7) years, and 46% were men. There was a significant association between vitamin B2 intake and all three cognitive scores (IRT, Odds Ratio = 0.77, 95% confidence interval: [0.65,0.92]; AFT, 0.75, [0.64,0.88]; DSST, 0.72, [0.59,0.88]). Moreover, vitamin B2 intake above the RDA reduced the risk of low cognitive performance (IRT, 0.66, [0.46,0.93]; AFT, 0.83, [0.62,1.11]; DSST, 0.65, [0.45,0.92]) compared to intake below the RDA. Dose-response curves indicated that higher vitamin B2 intake was negatively associated with the risk of low cognitive performance. Physical activity may modify the association between vitamin B2 and cognitive performance. Vitamin B2 intake was positively associated with cognitive performance among older adults. Adequate vitamin B2 intake could help protect cognitive function.

Deep learning model for solar and wind energy forecasting considering Northwest China as an example

The growing demand for renewable energy sources like wind and solar power requires accurate and reliable forecasting techniques for effective planning and operation. This study presents an attention-based spatial-temporal graph neural network–long short-term memory (ASTGNN-LSTM) model designed to predict wind speed and solar radiation using 20 years of meteorological data from five regions in Northwest China. The ASTGNN-LSTM model shows significant performance improvements over traditional methods, such as the historical average model, autoregressive integrated moving average model, and graph convolutional network with LSTM. After optimizing the hidden layers and learning rate, the relative errors for predicting wind speed and solar radiation are reduced to 27.15 % and 6.11 %, respectively. Sensitivity analysis reveals that location data have the most significant impact on predictions. These findings demonstrate that the ASTGNN-LSTM model effectively captures nonlinear relationships and can enhance renewable energy planning and management.

Hybrid optimization-based secured routing in mobile ad-hoc network

Mobile Ad hoc Networks (MANETs) pose significant routing challenges due to their decentralized and dynamic nature. Node mobility results in frequent changes in network topology, leading to unsFIG connectivity and link quality. Traditional RPs designed for static networks are inadequate for MANETs. To deal with these issues, a secure routing approach is proposed using the Red Panda-Lyrebrid Optimization (RePLO) algorithm, which combines the advantages of the Red Panda Optimization (RPO) and Lyrebird Optimization Algorithm (LOA) algorithms. The proposed approach consists of five steps: (i) configuring the system model, (ii) developing the energy model, (iii) creating the mobility model, (iv) selecting cluster heads using the RePLO algorithm, and (v) routing using the RePLO algorithm. The RePLO algorithm optimizes cluster head selection and routing while considering specific constraints such as delay, distance, energy, & security for Cluster Head (CH) selection, and link quality and enhanced trust for routing optimization. The effectiveness of the proposed approach is evaluated using various metrics to demonstrate its efficiency in MANET routing. By integrating multiple optimization techniques and considering critical constraints, the RePLO algorithm offers a systematic and secure solution for MANET routing. The evaluation results confirm the efficacy of the proposed approach in improving network performance, reliability, and security. Overall, the RePLO algorithm presents a promising approach to tackle the routing issues inherent in MANETs, paving the way for more robust and efficient communication in mobile ad hoc networks.

Modeling energy storage in long-term capacity expansion energy planning: an analysis of the Italian system

This paper presents a framework to represent short-term operational phenomena associated with renewables capacity factors and final service demand distributions in a capacity-expansion and integrated energy system optimization model. The aim is to study the potential role of energy storage technologies coupled with renewable energy sources aiding the decarbonization of the overall energy system. The proposed methodology is implemented in an energy system optimization model named Tools for Energy Model Optimization and Analysis (TEMOA) and then tested in a case study focused on the Italian energy system. We examine a collection of scenarios that includes reference time scale scenarios, time scale sensitivity scenarios, and technology alternative scenarios. This paper's findings indicate that energy storage is crucial for fully decarbonizing the Italian power sector by 2050 in the absence of a low-carbon baseload. Additionally, it suggests that approximately 10 % of Italy's electricity generation in 2050 should be routed through short-term energy storage devices.

Constraints on Metastable Dark Energy Decaying into Dark Matter

We revisit the proposal that an energy transfer from dark energy into dark matter can be described in field theory by a first order phase transition. We analyze a metastable dark energy model proposed in the literature, using updated constraints on the decay time of a metastable dark energy from recent data. The results of our analysis show no prospects for potentially observable signals that could distinguish this scenario from the ΛCDM. We analyze, for the first time, the process of bubble nucleation in this model, showing that such model would not drive a complete transition to a dark matter dominated phase even in a distant future. Nevertheless, the model is not excluded by the latest data and we confirm that the mass of the dark matter particle that would result from such a process corresponds to the mass of an axion-like particle, which is currently one of the best motivated dark matter candidates. We argue that extensions to this model, possibly with additional couplings, still deserve further attention as it could provide an interesting and viable description for an interacting dark sector scenario based in a single scalar field.

Analysis and Modelling for Industrial Energy Efficiency in the Cosmetics Industry: A Real Industrial Case in an Italian Manufacturing Company

Economic and environmental issues translating into energy costs and pollution within the production environment are increasingly attracting attention. Industrial Energy Efficiency (IEE) is gaining ever-higher importance within production environments. Since cosmetic consumers and companies are becoming increasingly sensitive to sustainability, the cosmetic field is working to reduce the environmental and social impact along the whole supply chain. Furthermore, IEE actions in business processes can lead to several medium- and long-term economic and environmental benefits. This paper is the first work in the literature presenting a real-case application of energy analysis and modeling to achieve better energy performances in a cosmetics production process. Thus, in the body of knowledge, it contributes by providing a real case of good practice to be benchmarked for future IEE interventions in cosmetics manufacturing processes. The work has been conducted by analyzing the production process’s energy consumption and developing an energy model of a selected machine (i.e., a turbo-emulsifier). The analysis and modeling performed aimed at assessing the different operational phases of the machine and evaluating the different behaviors of the data. Finally, the results allowed us to propose possible improvements to be applied to the production process to achieve better energy performances.

Comparison of Deterministic, Stochastic, and Energy-Data-Driven Occupancy Models for Building Stock Energy Simulation

Accurate modelling of occupancy patterns is critical for reliable estimation of building stock energy demand, which is a key input for the design of district energy systems. Aiming to investigate the suitability of different occupancy-modelling approaches for the design of district energy systems, the present study examines a set of standard-based schedules (from the UK National Calculation Methodology), a widely used stochastic occupancy model, and a novel energy-data-driven occupancy model. To this end, a dynamic energy model of a higher education office building developed within a stock model of London’s Bloomsbury district serves as a testbed to implement the occupancy models, explore their implications for the estimation of annual and peak heating and cooling demand, and extrapolate the findings to the computationally demanding building stock stimulations. Furthermore, the simulations were conducted in two years before and after the COVID-19 pandemic to examine the implications of hybrid working patterns after the pandemic. From the results, the energy-data-driven model demonstrated superior performance in annual heating demand estimations, with errors of ±2.5% compared to 14% and 7% for the standard-based and stochastic models. For peak heating demand, the models performed rather similarly, with the data-driven model showing 28% error compared to 29.5% for both the standard-based and stochastic models in 2019. In cooling demand estimations, the data-driven model yielded noticeably higher annual cooling demand and lower peak cooling demand estimations as compared with the standard-based and stochastic occupancy models. Given the adopted building-modelling approach, these findings can be extended to district-level investigations and inform the decision on the choice of occupancy models for building stock energy simulation.

Awareness-guided incremental control optimization for chilled water system with deep learning model under cold-start scenarios

For heating, ventilation, and air conditioning (HVAC) systems, the technology of optimal control can achieve significant energy savings by resetting the control setpoint in responding to the change of user load and weather condition. However, traditional method will fail in cold-start scenarios, in which the diversity of history data is limited. Under this case, significant prediction error will occur in extrapolation space, and the optimized control strategy cannot fully achieve energy saving potential or even increase energy consumption. To solve this problem, here we propose a novel awareness-guided incremental control optimization method. Its basic idea is to explicitly consider the prediction error of energy model during the control optimization process. The deep ensemble algorithm is firstly adopted to capture the prediction error. And the optimization process will be aware of such potential error, and make a tradeoff between conservative state and exploratory state. The control optimization process will start with the known optimal setpoint (conservative state) to achieve stable energy saving, and then gradually explores other unknown candidates (exploratory state) to achieve further energy saving. To validate our method, a virtual test bed of chilled water system is developed by Modelica language to compare fixed setpoint strategy, traditional method, and proposed method. The results demonstrate that the proposed method can avoid negative energy saving ratio during the early stage of control optimization. And it can also find optimal control strategy faster. Comparing with traditional method, the awareness-guided method can achieve further 2.9 % energy saving ratio, and the overall energy saving is between 6.1% and 13.9 %.

Future energy landscapes: Analyzing the cost-effectiveness of nuclear-renewable integrated energy Systems in Retrofitting of coal power plants

Coal Power Plants (CPPs), despite their substantial contribution to global energy needs, pose significant environmental concerns due to Greenhouse Gas (GHG) emissions. Thus, the world has started thinking of alternative generation sources to replace CPPs. To replace CPPs, some energy generation resources must come into the scenario that can outshine the advantages of CPPs, such as easy availability of fuel, operational safety, and cost effectiveness. Concerning this matter, nuclear-renewable integrated systems can play a vital role as a potential replacement for CPPs. In this study, we systematically explore the transitional approach from CPPs to advanced energy systems and conduct an exhaustive comparative analysis focusing on three proposed energy system models: Greenfield, Coal-to-Nuclear (C2N), and Coal-to-Integrated Energy Systems (C2IES). Before conducting the comparative analysis, we determine the most feasible coal sites from Alaska, our surrogate location for this study, using a GIS-based nuclear reactor siting tool named “Siting Tool for Advanced Nuclear Development (STAND).” To carry out the comparative analysis among the proposed energy models for the selected coal site, we find out the optimal configuration of each system using a robust and recent nature-based metaheuristic optimization algorithm, Mountain Gazelle Optimization (MGO), complemented by another nature-based metaheuristic optimization algorithm, Particle Swarm Optimization (PSO), for validation. The key data used in this study include solar irradiance, temperature, wind speed, load profiles, and comprehensive cost data for each system component. Since the proposed energy models are highly complex and consider several assumptions, the key research findings are strengthened by performing a comprehensive sensitivity analysis. The base case results show that C2IES can reduce the Cost of Energy (COE) by roughly 65 % compared to Greenfield and C2N and ensure the utmost reliability of the energy system. Although this cost-saving margin contrasts in the sensitivity analysis across a range of scenarios, C2IES consistently offers the most cost-effective solution, highlighting its potential for sustainable energy transition.

Artificial Neural Network-Based Route Optimization of a Wind-Assisted Ship

The International Maritime Organization aims for net-zero carbon emissions in the maritime industry by 2050. Among various alternatives, route optimization holds an important place as it does not require any additional component-related costs. Especially for wind-assisted ships, the effectiveness of different sailing systems can be improved significantly through route optimization. However, finding the ship’s optimal route is computationally expensive when the totality of possible weather conditions is taken into consideration. To determine the optimal route that minimizes energy consumption, an energy model based on the environmental conditions, ship route and ship speed was built using artificial neural networks. The energy consumed for given input data was calculated using a ship dynamics model and a database was generated to train the artificial neural networks, which predict how much energy is consumed depending on the route followed in given environmental conditions. Then, such networks were exploited to derive the optimal routes for all the relevant operational conditions. It was found that route optimization can reduce the overall ship energy consumption depending on the weather conditions of the environment by up to 9.7% without any increase in voyage time and by up to 35% with a 10% delay in voyage time. The proposed methodology can be applied to any ship by training real weather conditions and provides a framework for reducing energy consumption and greenhouse gas emissions during the service life of ships.

Erosion and transport mechanisms of granular flow: insights from a flume experiment and block energy model

Erosion and transport mechanisms of granular flow: insights from a flume experiment and block energy model

Fine-tuning weibull distribution parameters in Morocco’s Tarfaya and Tangier wind farms using two-stage swarm optimization

This study assesses the efficacy of particle swarm optimization (PSO) in estimating scale ( c) and shape ( k) parameters of the Weibull distribution model for wind energy forecasting at two key wind farm sites in Morocco— Tarfaya in the south and Tangier in the north, utilizing real wind data from 2022. Employing a novel square frequency error objective function to enhance parameter accuracy, the study adopts a two-stage training approach involving recursive least-square estimation and PSO fine-tuning. Validation with artificial data underscores PSO’s effectiveness under diverse wind conditions. Parameter sensitivity analysis identifies four optimal PSO configurations, with the PSO-4 model exhibiting superior performance. Comparative analysis against traditional and heuristic optimization methods consistently demonstrates PSO-4’s lowest root-mean-square error (RMSE) and mean absolute error (MAE), high coefficients of determination ( R2), and shortest computation time. The research highlights PSO-4 model as a precise and efficient tool for Weibull distribution parameter estimation in wind energy forecasting, showcasing robust convergence across both wind farm sites.

Insights into muscle metabolic energetics: Modelling muscle-tendon mechanics and metabolic rates during walking across speeds.

The metabolic energy rate of individual muscles is impossible to measure without invasive procedures. Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled. This study aimed to examine how muscle-tendon model individualization and metabolic energy models influenced estimation of muscle-tendon states and time-series metabolic rates, to evaluate the agreement with empirical data, and to provide predictions of the metabolic rate of muscle groups and gait phases across walking speeds. Three-dimensional musculoskeletal simulations with prescribed kinematics and dynamics were performed. An optimal control formulation was used to compute muscle-tendon states with four levels of individualization, ranging from a scaled generic model and muscle controls based on minimal activations, inclusion of calibrated muscle passive forces, personalization of Achilles and quadriceps tendon stiffnesses, to finally informing muscle controls with electromyography. We computed metabolic rates based on existing models. Simulations with calibrated passive forces and personalized tendon stiffness most accurately estimate muscle excitations and fiber lengths. Interestingly, the inclusion of electromyography did not improve our estimates. The whole-body average metabolic cost was better estimated with a subset of metabolic energy models. We estimated metabolic rate peaks near early stance, pre-swing, and initial swing at all walking speeds. Plantarflexors accounted for the highest cost among muscle groups at the preferred speed and were similar to the cost of hip adductors and abductors combined. Also, the swing phase accounted for slightly more than one-quarter of the total cost in a gait cycle, and its relative cost decreased with walking speed. Our prediction might inform the design of assistive devices and rehabilitation treatment. The code and experimental data are available online.

Phase-field modeling of interdiffusion between dissimilar Fe-Cr-Ni alloys during non-isothermal hot isostatic pressing

Powder metallurgy hot isostatic pressing (PM-HIP) has emerged as a promising alternative to welding for joining dissimilar metals. During HIP, interfacial bonding is mediated by solid state diffusion. The interdiffusion zone across the interface depends on processing conditions, calling for the need for accurate numerical tools capable of simulating interdiffusion and possible phase transformation in order to optimize processing parameters. Here, a phase-field (PF) model based on CALPHAD-based free energy functionals is developed to simulate the interdiffusion and phase evolution between dissimilar Fe–Cr–Ni based steels undergoing HIP and is demonstrated using the interface between 316L and SA508 steels. To overcome the numerical challenges caused by the singular magnetic and entropy terms in the CALPHAD free energy models in the Fe–Cr-Ni system, polynomial functions are fitted with temperature dependent coefficients represented by Fourier series to accurately describe the phase stability of both fcc and bcc phases in the composition and temperature space. This enables simulations of non-isothermal HIP cycles. Diffusivity data from commercial software and literature are taken to parameterize the kinetic parameters. A discrete nucleation model is incorporated for possible phase transformation. The modified thermodynamic models are validated against previous experiments at 923 K and 1273 K. The interdiffusion kinetics are benchmarked against new HIP experiments joining powder and bulk 316L to bulk SA508 with three different HIP cycles. The good agreement between simulations and experiments on both phase stability and interdiffusion indicate that the model is suitable for simulating interdiffusion between Fe–Cr–Ni alloys during HIP cycles. It is also found that using powder and bulk 316L gives similar interdiffusion profiles at elevated temperature when a dense interface forms during HIP.

Image texture analysis of pellets made of lignocellulosic materials

This experiment aimed to find the relation between image texture features of pellets made of various lignocellulosic materials (wood, wheat straw, hay, giant miscanthus, prairie spartina, and giant knotweed) and their physico-mechanical properties (density, compressive energy, maximum compressive strength, modulus of elasticity). Using the Kruskal-Wallis's test, the effect of materials on these properties was examined. Texture features were derived from the grey-level co-occurrence matrix, grey-level run-length matrix, absolute gradient matrix, autoregressive model, and wavelet decomposition, resulting in 86 features, later reduced to 8 factors via explanatory factor analysis. These factors were used as predictors in regression models for physico-mechanical properties. The models for modulus of elasticity achieved R2adj values of 0.91–0.99 (except for hay and wood), compressive stress models achieved 0.65–0.99 (excluding hay and wood), compressive energy models ranged from 0.60 to 0.97 (excluding hay), and density models ranged from 0.56 to 0.97 (excluding wood). The study confirmed a significant correlation between material type, texture parameters and compression resistance, suggesting this method could monitor pellet quality in production.

Generalized ghost pilgrim dark energy model in f(ℚ,) theory

This study investigates the reconstruction technique for the generalized ghost pilgrim dark energy model within the framework of f(Q,T) gravity ( Q represents the non-metricity and T denotes the trace of the energy-momentum tensor). For this purpose, we implement a correspondence scheme for the configuration of dust fluid within a flat Friedmann-Robertson-Walker universe. We examine the cosmic behavior of the reconstructed model by studying cosmological diagnostic parameters in conjunction with phase planes. The analysis reveals that within certain ranges of free parameters, the equation of state parameter characterizes a phantom regime. The trajectories on the ωDE−ωDE′ plane indicate a freezing region, while the phase plane of r − s corresponds to the Chaplygin gas model. The squared sound speed parameter provides stable model for the evolutionary paradigm of the Universe. It is worth noting that our results closely align with the recent observational data.