Proposed Energy Model Research Articles

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.

An energy-based 2D model for predicting mechanical behavior of adhesively bonded CFRP laminates

This paper presents a two-dimensional energy-based model for the mechanical behavior of adhesively bonded carbon fiber-reinforced polymer (CFRP) joints subjected to tensile loading. The proposed energy model is better suited than available discrete models to capture failure modes in adhesively bonded CFRP joints of different configurations having complex geometries. The governing equations are first developed for a three-stepped lap joint configuration by employing the minimum potential energy theorem and then solved using the finite element method (FEM). The disbond behavior of the joint configuration is modeled using the bilinear cohesive law. Subsequently, the developed model is extended to capture the mechanical response of adhesive joints for simple lap and scarf joint configurations. The proposed energy model is verified against 3D-FEA and experimental studies. Thereafter, a comparative analysis is undertaken for the three configurations of adhesive joints studied here. This is followed by a parametric study for the three-stepped lap joint to understand the influence of geometric and material properties of the adhesive and adherends on the stiffness and load-carrying capacity of the adhesively bonded CFRP joint. Finally, a design criterion is proposed for the three-stepped lap joint configuration, aimed at improving its load-carrying capacity/strength by reducing the peak stress levels at joint corners/overlap edges.

3R: A reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead, and energy efficiency. This paper proposes 3R, a reliable multi agent reinforcement learning routing protocol for WMSN. 3R uses a novel resource-conservative Reinforcement Learning (RL) model to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, an energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model only uses local information to avoid the resource burdens and the security concerns of exchanging energy information. Experimental results prove the lightweightness, attacks resiliency and energy efficiency of 3R, making it a potential routing candidate for WMSN.

Electro-mechanical-thermal energy coupling characteristics analysis and EFMM-based energy modelling for machine tools considering dynamic thermal environment

Machine tool is an isolated thermodynamic and inefficient energy-consuming system, whose energy consumption is increasingly influenced by its internal thermal environment. While the thermal environment changes dynamically due to the complex electro-mechanical-thermal (EMT) energy conversion characteristics. Therefore, it is urgent to expound the multi-energy coupling mechanism to establish an accurate energy model for machine tool. However, the existing studies merely emphasize the electro-mechanical conversion that related to the material-removal (MR) process and fail to reveal the dynamic characteristics of energy caused by EMT, which results in inaccurate energy models. In light of this, the energy flow matrix modelling (EFMM) method, which can model the multi-energy flows automatically and accurately, is introduced to construct a generic energy balance matrix equation for machine tool, where the coupling relationship between electrical, mechanical and thermal energy are taken into consideration. Subsequently, the dynamic mechanical and thermal energy are analyzed, respectively. Based on this, the total energy consumption and cutting energy efficiency models for machine tool in a dynamic thermal environment are established. The reliability and accuracy of the constructed models are demonstrated by the case study and comparative analysis. The results indicate that the average relative errors of the proposed energy models are mostly within 5 %. The presented approach is conducive to understanding the multi-energy coupling characteristics and providing a feasible way for energy-efficient machine tools.

Maximum AoI Minimization for Target Monitoring in Battery-Free Wireless Sensor Networks

Age of Information (AoI) has been proposed to measure the freshness of the sensory data for IoT applications. In Battery-free WSNs (BF-WSNs), The AoI minimization data collection problem has been extensively studied. Apart from data collection, target monitoring is also an important application for BF-WSNs. To capture the freshness of the sensory data, the AoI of the sensory data related to targets (The AoI of targets) needs to be considered. To guarantee the performance of the target monitoring system, the maximum AoI of all targets should be minimized. However, existing works mainly investigated the AoI of the sensory data related to nodes (The AoI of nodes). Also, existing energy models are not practical enough for battery-free nodes. To deal with those problems, in this paper, we first propose a more practical energy model. Then the problem of Maximum AoI minimization for Target monitoring in Battery-free WSNs (MTB) is formally defined based on the proposed energy model. A two-stage algorithm is proposed to solve MTB optimally, in which all nodes in the network are scheduled collaboratively to monitor all the targets in the monitoring field. Extensive simulations and real-world experiments verify the high performance of our algorithm and energy model.

Analysis of a Solar Hybrid Electricity Generation System for a Rural Community in River State, Nigeria

This paper presents the technical and economic analysis of a solar–wind electricity generation system to meet the power requirements of a rural community (Okorobo-Ile Town in Rivers State, Nigeria) using the Renewable—energy and Energy—efficiency Technology Screening (RETScreen) software. The entire load estimation of the region was classified into high class, middle class, and lower class. Two annual electricity export rates were considered: 0.1 USD/KWh and 0.2 USD/KWh. The results from the proposed energy model comprising a 600 kW PV system and a 50 kW wind system showed that with a USD 870,000 initial cost and USD 9600 O&M cost, the annual value of the electricity generated was 902 MWh. The simple payback was 5.1 years with a net present value of USD 3,409,532 when 0.2 USD/KWh was used as the annual export rate instead of 10.8 years for simple payback and an NPV of USD 1,173,766 when 0.1 USD/KWh was used. Thus, there is a potential to install a wind–solar system with average weather conditions of 4.27 kWh/m2/d for the solar irradiance and 3.2 m/s for the wind speed at a 10 m hub height using a rate of 0.2 USD/KWh as the electricity export rate.

A multi-dimension coupling model for energy-efficiency of a machining process

Energy-efficient machining has become imperative for energy conservation of manufacturing sectors. The energy characteristics of machining process tend to be very complex, varying substantially with respect to different configurations of machine tool, workpiece and process parameters. This paper undertakes this challenge and explores the energy consumption characteristics of machining process adaptive to different machine tools, workpieces and process parameters. A multi-dimension coupling model of energy consumption for machining process is first established by considering specifications of machine tools, workpieces and processes. Then the influence factors of energy consumption are systematically analyzed from a multi-dimensional perspective. The internal interact relationship among each dimensional parameter is illustrated. To validate the effectiveness of the proposed energy model and determine the energy-efficient machining configurations with related to machine tools, workpieces and process parameters, a series of experiments are carried out on a CNC vertical machining center. Experimental results show that the optimal machining configurations can effectively reduce energy consumption and simultaneously improve energy-efficiency of CNC machining.

Agrobiological justification of agricultural specialization of the region — a model of agricultural production (on the example of Tetiiv district of Kyiv region)

Purpose. Identify and justify agrobiological mechanisms for optimizing the agricultural system of the region. Methods. Field, laboratory and chemical, mathematical and statistical. Results. Assessment of sowings in crop rotations was carried out in the years 2017–2020 at the State Enterprise Experimental Farm Shevchenkivske IBCSB NAAS of Ukraine (Tetiiv district, Kyiv region). The following tasks were performed: potential and terms of crop yield in crop rotations, increasing soil fertility, development of scientifically sound crop rotations, fertilizer application, chemical reclamation of soil, soil protection measures, minimal tillage, integrated plant and pest control, agronomic practices. Conclusions. The proposed energy model of intensive agriculture in the region provides for the optimisation of the agricultural landscape structure and sown areas, the spread of the most soil-saving crops, crop rotation, varieties of the new generation. To this end, it is necessary to withdraw degraded and unproductive lands from production and turn them to hayfields, pastures, forest plantations. Under such conditions, it becomes possible to increase the share of grain crops to 75–78% and sugar and fodder beets to 80–83%.

Recycling c-Si PV Modules: A Review, a Proposed Energy Model and a Manufacturing Comparison

As human activities are increasingly exploiting our planet’s scarce resources, managing them has become of primary importance. Specifically, this study examines the management of photovoltaic (PV) waste that is produced when PV modules reach end-of-life (EoL). PV modules contain precious and valuable materials, as well as toxic materials that may be harmful to human health and the environment if not disposed of properly. First, this study aims to review and analyze the current literature in order to gain a deeper understanding of the recycling of PV modules, particularly c-Si modules, which represent the largest market share. In the second part, an analysis is conducted of the energy consumption of these recycling processes using a proposed model based on the full recovery end-of-life photovoltaic (FRELP) process. PV modules manufactured from raw materials and PV modules manufactured from recycled materials are also compared in this section. In addition, improvements are suggested with respect to the design of PV modules (eco-design). According to this study, c-Si PV modules can be recycled with an energy consumption as low as 130 ÷ 300 kWh/ton of treated PV waste, estimating an overall recycling yield of about 84%.

Benchmarking Neuromorphic Hardware and Its Energy Expenditure.

We propose and discuss a platform overarching benchmark suite for neuromorphic hardware. This suite covers benchmarks from low-level characterization to high-level application evaluation using benchmark specific metrics. With this rather broad approach we are able to compare various hardware systems including mixed-signal and fully digital neuromorphic architectures. Selected benchmarks are discussed and results for several target platforms are presented revealing characteristic differences between the various systems. Furthermore, a proposed energy model allows to combine benchmark performance metrics with energy efficiency. This model enables the prediction of the energy expenditure of a network on a target system without actually having access to it. To quantify the efficiency gap between neuromorphics and the biological paragon of the human brain, the energy model is used to estimate the energy required for a full brain simulation. This reveals that current neuromorphic systems are at least four orders of magnitude less efficient. It is argued, that even with a modern fabrication process, two to three orders of magnitude are remaining. Finally, for selected benchmarks the performance and efficiency of the neuromorphic solution is compared to standard approaches.

ЭНЕРГЕТИЧЕСКИЕ АСПЕКТЫ ИЗМЕЛЬЧЕНИЯ ФЕРРОМАГНИТНЫХ ЧАСТИЦ ШЛИФОВАЛЬНОГО ШЛАМА ВО ВРАЩАЮЩЕМСЯ ЭЛЕКТРОМАГНИТНОМ ПОЛЕ

The work objective. Study of energy conditions for grinding ferromagnetic particles by non-contact method in devices with a rotating electromagnetic field.
 The problem to which the article is devoted. Development of an energy model for controlling the process of grinding ferromagnetic particles of sludge wastes in a rotating electromagnetic field and experimental confirmation of its relevance.
 Research methods. Theoretical studies are based on the main principles of the theory of magnetism, strength and destruction of solids. Experimental studies were carried out at the Research Institute of "Vibrotechnologii" of Don State Technical University using a pilot plant implementing a rotating electromagnetic field.
 The novelty of the work. Establishment of analytical dependences describing the energy aspects of grinding ferromagnetic particles in a non-contact manner in devices with a rotating electromagnetic field.
 The results of the study. A dependence is proposed that makes it possible to estimate the energy state of particles in a magnetic vibrating bed formed in devices under the influence of a rotating electromagnetic field. The energy condition determining the threshold value of the field induction gradient is established, which ensures a stable mode of the magnetic vibrating layer, and, as a consequence, grinding of particles to a given size. An energy model for controlling the grinding process of ferromagnetic particles of sludge wastes in a rotating electromagnetic field is developed and probability-theoretic formulas for estimating the duration of the grinding process is proposed. 
 Conclusions. The use of devices with a rotating electromagnetic field allows to implement effectively one of the important stages of treating metal sludge wastes associated with grinding of its ferromagnetic particles, which ensures the production of ferromagnetic raw materials with high granulometric characteristics. 
 The proposed energy model of particle grinding in a rotating electromagnetic field, based on the balance of the energy of destruction and the energy received by the particle from the electromagnetic field, taking into account the mechanical and magnetic characteristics of the material, makes it possible to implement the process of controlling the granulometric size of ferromagnetic particles of sludge wastes.

Energy-Efficient Dataflow Scheduling of CNN Applications for Vector-SIMD DSP

Dataflow-scheduling techniques for convolutional neural networks (CNNs) are extensively studied to minimize the off-chip memory access. However, the efficiencies of the previously proposed techniques are limited because their optimizations only consider the general hardware such as FPGA and GPU. To overcome this limitation, this paper proposes dataflow scheduling for vector-SIMD DSP to minimize the energy consumption for the off-chip memory access. First, the proposed technique attempts to group as many given layers as possible. For grouping the layers, the tiles in different layers are executed in sequence without the off-chip memory access except the first and the last layers in the group. The length of the grouped layers is determined with regard to the minimization of the energy consumption of off-chip memory by estimating the proposed energy model of the off-chip memory. However, grouping the layers results in the additional computation. To minimize this overhead, this paper solves the optimization problem for in the grouped layers. Second, for layers that cannot be grouped, the tiling along the W-axis is not considered, to maximize the size of the overlapped data in consecutive tiles. Consequently, the reuse of the overlapped data in the on-chip buffer is maximized, thereby reducing the energy consumption by the off-chip memory. For evaluation, a cycle-accurate simulation environment is established to measure the energy consumption of the off-chip memory by tracing the data between a vector-SIMD DSP and an off-chip memory. The experimental results show that compared with the baseline tiling and scheduling techniques, the proposed technique reduces the energy consumption by an average of 51% for CNN applications such as Tiny YOLOv2, MobileNetv1, VDSR.

Empirical power consumption characterization and energy aware scheduling in data centers

Energy-efficient management is key to reduce operational cost and environmental contamination in modern data centers. Energy management and renewable energy utilization are strategies to optimize energy consumption in high-performance computing. In any case, understanding the power consumption behavior of physical servers in datacenter is fundamental to implement energy-aware policies effectively. These policies should deal with possible performance degradation of applications to ensure quality of service. This manuscript presents an empirical evaluation of power consumption for scientific computing applications in multicore systems. Three types of applications are studied, in single and combined executions on Intel and AMD servers, for evaluating the overall power consumption of each application. The main results indicate that power consumption behavior has a strong dependency with the type of application. Additional performance analysis shows that the best load of the server regarding energy efficiency depends on the type of the applications, with efficiency decreasing in heavily loaded situations. These results allow formulating models to characterize applications according to power consumption, efficiency, and resource sharing, which provide useful information for resource management and scheduling policies. Several scheduling strategies are evaluated using the proposed energy model over realistic scientific computing workloads. Results confirm that strategies that maximize host utilization provide the best energy efficiency.

Energy performance and climate control in mechanically ventilated greenhouses: A dynamic modelling-based assessment and investigation

Controlled environment agriculture in greenhouse is a promising solution for meeting the increasing food demand of world population. The accurate control of the indoor environmental conditions proper of greenhouses enhances high crop productivity but, contemporarily, it entails considerable energy consumption due to the adoption of mechanical systems. This work presents a new modelling approach for estimating the energy consumption for climate control of mechanically ventilated greenhouses. The novelty of the proposed energy model lies in its integrated approach in simulating the greenhouse dynamics, considering the dynamic thermal and hygric behaviour of the building and the dynamic response of the cultivated crops to the variation of the solar radiation. The presented model simulates the operation of the systems and the energy performance, considering also the variable angular speed fans that are a new promising energy-efficient technology for this productive sector. The main outputs of the model are the hourly thermal and electrical energy use for climate control and the main indoor environmental conditions. The presented modelling approach was validated against a dataset acquired in a case study of a new fully mechanically controlled greenhouse during a long-term monitoring campaign. The present work contributes to increase the knowledge about the dynamics and the energy consumption of greenhouses, and it can be a valuable decision support tool for industry, farmers, and researchers to properly address an energy efficiency optimisation in mechanically ventilated greenhouses to reach the overall objective of decreasing the rising energy consumption of the agricultural sector.

Energy Optimization in Multi-Hop Wireless Sensor Networks based on Proposed Harmony Search Routing Algorithm

Spread application of wireless sensor networks (WSNs), has led many researchers aiming at energy optimization in these networks. The Proposed Energy Efficient Harmony Search Routing Algorithm (PEEHSRA) in accordance with a novel energy model is stated in this study to minimize the amount of energy consumption in WSNs. We first proposed an energy model on the basis of modulation scheme parameters for multi-hop transmission. Then, in order to develop a harmony search routing algorithm, pitch adjustment rate in the harmony memory was introduced based on both, the proposed energy model, and the node’s distance. Also, an energy-efficient fitness function was determined, which results in the most energy-efficient route for the network. At the end, four modulations including MQAM, MQPSK, OQPSK and MFSK were applied and the most energy-efficient one with corresponding parameters was specified. By comparing the results of the proposed method to the state-of-the-art studies, the improvements in extending the lifetime of the network was proved. These findings seem quite useful in designing an energy-efficient multi-hop WSN and pave the way for enhancement of the widespread applications in these networks.

Building heat consumption and heat demand assessment, characterization, and mapping on a regional scale: A case study of the Walloon building stock in Belgium

Energy consumption in buildings results in CO2 emissions and it is necessary to reduce energy consumption thus its related emissions. This research is included in the Wal-e-cities project, which is funded by the European Regional Development Fund (ERDF) and aims to create tools that facilitate the transition toward smart territory. The annual heat consumption (HC) and heat demand (HD) of Wallonia building stock of more than 1,700,000 buildings are assessed. Subsequently, the developed energy models are coupled with a geographic information system (GIS) to calculate and map the HC and HD. The HC and HD are calculated for each building and are represented by different levels of territorial aggregation, namely neighbourhood, municipality, and urban region scales. The highest HC values were observed in large cities and main industrial areas, whereas the lowest values were observed in rural areas. For residential sector, HC is mainly related to the number of dwellings, which differs from that of tertiary and industrial sectors where HC also depends on the nature and function of buildings. Based on mean values at the neighbourhood scale, the HD is 16.44% lower than the HC for the residential sector, 15.78% lower than the HC for the tertiary sector, and 9.26% lower than the HC for the industrial sector. The proposed energy models are validated. The relative differences between annual HC calculated in this study and that provided in the regional energy reports are −5.82% for the residential sector, −14.29% for the tertiary sector, and −2.02% for the industrial sector.

Energy consumption considering tool wear and optimization of cutting parameters in micro milling process

Micro milling process aims to manufacture complex micro/meso structures, and the reduction of material removal volume determines the possible decrease of the total energy use, which would put less pressure on the environment. However, the energy consumption of micro milling influenced by tool wear and tool run-out would be augmented and result in the drawback of more energy consumption. In order to reduce the energy consumption of micro milling process, a new analytical energy consumption model and the related optimization of cutting parameters are presented in this paper. Although the influence of tool wear is inevitable, it hasn't been thoroughly concerned in the existing energy consumption models. Therefore, the stochastic tool wear progression, which can be obtained from a probabilistic approach based on the online measured cutting forces, is integrated into the proposed energy model. In addition, the process nonlinearities caused by tool run-out and the trochoidal trajectories of cutting edge are also considered in the model. With the developed prediction model of energy consumption, a hybrid cuckoo search and grey wolf algorithm is used to determine the optimum cutting parameters for minimizing the total energy consumption. The micro milling experiments are performed to validate the accuracy and availability of the proposed energy consumption model and the optimization method. The improved optimization method based on the proposed energy model can reduce the energy consumption by 7.89% compared with the empirical selection.

Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols

In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.

Energy Model for Motion Planning of 2D-Belt Press Line Tending Robots

A current trend in production is to reduce energy consumption where possible not only to lower the cost but also to be a more energy efficient entity. This paper presents an energy model to estimate the electrical energy consumption of 2D-belt robots used for material handling in multi-stage sheet metal press lines. An estimation of the energy consumption is computed by the proposed energy model based on the robot components' specifications, the robot path and trajectory. The proposed model can predict the energy consumption offline by simulation, and thus, before installation, avoiding the need for physical experiments. It is demonstrated that it can be used for predicting potential energy reductions achieved by optimising the motion planning. Additionally, it is also shown how to investigate the energy saving achieved by using mechanical brakes when the robot is idle. This effectively illustrates the usefulness of the proposed energy model. [Submitted 01 March 2018; Accepted 18 March 2019]

High Frame Rate Video Reconstruction Based on an Event Camera.

Event-based cameras measure intensity changes (called 'events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the 'active pixel sensor' (APS), the 'Dynamic and Active-pixel Vision Sensor' (DAVIS) allows the simultaneous output of intensity frames and events. However, the output images are captured at a relatively low frame rate and often suffer from motion blur. A blurred image can be regarded as the integral of a sequence of latent images, while events indicate changes between the latent images. Thus, we are able to model the blur-generation process by associating event data to a latent sharp image. Based on the abundant event data alongside a low frame rate, easily blurred images, we propose a simple yet effective approach to reconstruct high-quality and high frame rate sharp videos. Starting with a single blurred frame and its event data from DAVIS, we propose the Event-based Double Integral (EDI) model and solve it by adding regularization terms. Then, we extend it to multiple Event-based Double Integral (mEDI) model to get more smooth results based on multiple images and their events. Furthermore, we provide a new and more efficient solver to minimize the proposed energy model. By optimizing the energy function, we achieve significant improvements in removing blur and the reconstruction of a high temporal resolution video. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable. Experimental results on both synthetic and real datasets demonstrate the superiority of our mEDI model and optimization method compared to the state-of-the-art.