Simulation Of Process Research Articles

Process simulation of laser shock-based disassembly of adhesively bonded composite/metallic structural parts: a numerical upscaling

ABSTRACT In previous works, both experimental and numerical investigations have shown the potential of the laser shock technique for disassembling adhesively bonded composite/metal coupons. This study extends this concept to upscale the method, focusing on the full disassembly of a foreign object damage (FOD) panel designed to replicate an aircraft engine fan blade. Utilizing LS-Dyna explicit FE software, the numerical simulation incorporates various material models: a progressive damage model for the 3D CFRP substrate, Johnson–Cook plasticity model combined with Grüneisen equation of state for the titanium layer, and a cohesive zone model with a bilinear traction separation law for the adhesive layer. Investigating the FOD panel’s inclined geometry, a preliminary analysis examines the impact of inclination angles on shock wave propagation and back face velocity, revealing minimal alterations. Initial simulations are conducted to determine double pulse delay times and evaluate spot location effects. Automation of the multi-step process simulation is achieved through a Python script. After approximately 30 shots, complete disassembly of the FOD strip is achieved, with observed damage primarily limited to matrix cracking in the composite substrate. Numerical findings support the efficacy of the double-shot laser shock method for disassembling adhesively bonded metallic/CFRP components, contingent upon appropriate experimental arrangements.

Machine learning for high-precision simulation of dissolved organic matter in sewer: Overcoming data restrictions with generative adversarial networks

Understanding the transformation process of dissolved organic matter (DOM) in the sewer is imperative for comprehending material circulation and energy flow within the sewer. The machine learning (ML) model provides a feasible way to comprehend and simulate the DOM transformation process in the sewer. In contrast, the model accuracy is limited by data restriction. In this study, a novel framework by integrating generative adversarial network algorithm-machine learning models (GAN-ML) was established to overcome the drawbacks caused by the data restriction in the simulation of the DOM transformation process, and humification index (HIX) was selected as the output variable to evaluate the model performance. Results indicate that the GAN algorithm's virtual dataset could generally enhance the simulation performance of regression models, deep learning models, and ensemble models for the DOM transformation process The highest prediction accuracy on HIX (R2 of 0.5389 and RMSE of 0.0273) was achieved by the adaptive boosting model which belongs to ensemble models trained by the virtual dataset of 1000 samples. Interpretability analysis revealed that dissolved oxygen (DO) and pH emerge as critical factors warranting attention for the future development of management strategies to regulate the DOM transformation process in sewers. The integrated framework proposed a potential approach for the comprehensive understanding and high-precision simulation of the DOM transformation process, paving the way for advancing sewer management strategy under data restriction.

An upscaling model for simulation of geothermal processes in stratified formations

In stratified porous media, non-uniform velocity between layers combined with thermal conduction across layers causes spreading of the thermal front: thermal Taylor dispersion. Conventional upscaling not accounting for this heterogeneity within simulation grid blocks underestimates thermal dispersion, leading to overestimation of thermal breakthrough time. We derive a model for effective longitudinal thermal diffusivity in the direction of flow, αeff, to represent the effective thermal dispersion in two-layer media. αeff, accounting for thermal Taylor dispersion, is much greater than the thermal diffusivity of the rock itself. We define a dimensionless number, NTC, a ratio of times for longitudinal convection to transverse conduction, as an indicator of transverse thermal equilibration of the system during cold- or hot-water injection. For NTC > 5, thermal dispersion in the two-layer system closely approximates a single layer with αeff. This suggests a two-layer medium satisfying NTC > 5 can be combined into a single layer with an effective longitudinal thermal diffusivity αeff. In application to a geothermal reservoir, one can apply the model to perform upscaling in stages, i.e. combining two layers satisfying the NTC criterion in each stage. The αeff model accounting for the fine-scale heterogeneity within simulation grid blocks would enhance the prediction accuracy of thermal breakthrough time and thus thermal lifetime.

Investigation of the Driving Force of Replacing Adsorbed Hydrocarbons by CO2 in Organic Matter from an Energy Perspective.

Carbon dioxide (CO2) has been widely used to enhance the recovery of adsorbed hydrocarbons from the organic matter (OM) in shale formations. To reveal the driving force of replacing adsorbed hydrocarbons from OM by CO2, we performed molecular dynamics (MD) simulations of the replacement process and calculated the interaction forces between CO2 and hydrocarbons. In addition, based on the umbrella sampling method, steered MD simulations were performed, and the free energy profiles of hydrocarbons were obtained using the weighted histogram analysis method. Results show that the condition of the hydrocarbon replacement by CO2 requires the hydrocarbon to have sufficient kinetic energy or to have a sufficiently large attractive force exerted to ensure that the hydrocarbon escapes the potential well of the OM. The attractive forces exerted on hydrocarbon molecules by CO2 can significantly decrease the energy barrier associated with hydrocarbon movement away from the OM surface. Furthermore, both CO2 and supercritical CO2 can effectively displace adsorbed hydrocarbon gas (methane) on the OM, while supercritical CO2 is required to enhance the recovery of adsorbed hydrocarbon oil (n-dodecane). The results obtained in this study provide guidance for enhancing the recovery of adsorbed hydrocarbons by CO2 in shale formations.

Dark fluxes from electromagnetic cascades

We study dark sector production in electromagnetic (EM) cascades. This problem requires accurate simulations of Standard Model (SM) and dark sector processes, both of which impact angular and energy distributions of emitted particles that ultimately determine flux predictions in a downstream detector. We describe the minimal set of QED processes which must be included to faithfully reproduce a SM cascade, and identify a universal algorithm to generate a dark sector flux given a Monte-Carlo simulation of a SM shower. We provide a new tool, , which simulates EM cascades with associated dark vector production, and compare it against existing literature and “off the shelf” tools. The signal predictions at downstream detectors can strongly depend on the nontrivial interplay (and modelling) of SM and dark sector processes, in particular multiple Coulomb scattering and positron annihilation. We comment on potential impacts of these effects for realistic experimental setups.

Expression of Concern: Simultaneous extraction and preliminary purification of polyphenols from grape pomace using an aqueous two-phase system exposed to ultrasound irradiation: process characterization and simulation

Expression of Concern: Simultaneous extraction and preliminary purification of polyphenols from grape pomace using an aqueous two-phase system exposed to ultrasound irradiation: process characterization and simulation

Additive Manufacturing Process Simulation of Laser Powder Bed Fusion and Benchmarks

As the aerospace industry continues to adopt additively manufactured (AM) parts for flight hardware, process simulation becomes more attractive to improve the manufacturing process by understanding how process parameters affect part quality and performance. Process simulation can also be used to predict and prevent build failures before the printing process. The powder bed fusion process of Ti-6Al-4V material is modeled using commercial finite element software to simulate the selective laser melting process. The thermal history is obtained from transient heat transfer analysis. Both the inherent strain approach and a sequential thermal-mechanical approach are employed to predict residual stress and part distortion. A National Agency for Finite Element Methods and Standards (NAFEMS) benchmark problem is presented as a numerical example. It is a thin wall structure with geometry features that can lead to part defects due to thermal distortion. It is shown that both analysis approaches are able to capture the thin-member bridging behavior, stepping behavior, and general distortion contour plot as those published by NAFEMS.

Process Simulation and Design of Green Ammonia Production Plant for Carbon Neutrality

Process Simulation and Design of Green Ammonia Production Plant for Carbon Neutrality

Modeling and investigation of combined processes of casting, rolling, and extrusion to produce electrical wire from alloys Al-Zr system

The results of modeling and research of casting, rolling, and extrusion processes for producing wire from Al-Zr system alloys and determining its physical and mechanical properties have been presented. When implementing combined processes for processing aluminum alloys, the number of operations is significantly reduced, productivity and yield are increased.As a result of the conducted research, the influence of alloy preparation modes and their chemical composition on the physical, mechanical and electrical properties of longish semifinished products from Al-Zr system alloys obtained by combined rolling-extrusion (CRE) and ingotless rolling-extrusion (IRE) methods were investigated.The simulation of the CRE process for one of these alloys of the system was carried out in the DEFORM 3D software package using data on its rheological properties obtained by the hot torsion method. Based on the modeling results, the optimal modes for conducting experimental studies were selected.The features of metal forming were experimentally studied, the temperature-velocity and technological parameters of combined processing were found, as well as the properties of cast billets, including those obtained using an electromagnetic mold (EMM). The results of experimental studies of the CRE and IRE processes suggest that it is possible to obtain longish deformed semifinished products from alloys Al-Zr system with a level of physical, mechanical and electrical properties that meet international standards.At all technological stages, including drawing, the structure of the metal was studied, and data on the physical and mechanical properties of hot-extruded rods and wire in cold-deformed and annealed states was obtained.The heat resistance of wire made from the investigated alloys was studied and it was found that after testing at a temperature of 280 °C and a holding time of 1 h, it satisfies the requirements of the AT3 type standard with a maximum permissible long-term operating temperature of 210 °C.Recommendations are given for industrial implementation; alloys containing 0.15-0.20% zirconium and 0.10-0.15% iron are recommended for the manufacture of AT1 type wire without heat treatment, as well as 0.25-0.30% Zr and 0.2-0.25% Fe for AT3 wire type with heat treatment.

Sizing requirements of the photovoltaic charging station for small electrical vehicles

Electric vehicles (EVs) are being introduced in Rwanda and becoming attractive for different reasons. For instance, these types of vehicles can help decrease air pollution and noise emissions. In addition, it presents an alternative to combustion engines, given the increased price of fuel resources in Rwanda and around the world. This paper presents a tool tailored to optimize the design of an electrical charging station serving small-sized electric vehicles, utilizing the algorithm to assist in sizing stand-alone mopped charging stations. The developed tool is based on the toolbox EventSim from MathWorks, which permits the combination of the simulation of discrete events (such as the arrival of customers at the station) with continuous states (such as the simulation of the charging process). The required PV power was estimated by utilizing solar resources, for the location, from renewables. Ninja. The number of customers arriving at the existing oil station is normalized to estimate the energy requirements of the mopped fleet. A Poisson distribution was proposed to model the battery discharge upon arrival, and different related parameters were evaluated through a sensitivity analysis to identify their effects on the performance of photovoltaic charging station. For the testing values, the station parameters were changed by ±25% to determine the impact of key design parameters on station performance, as well as other satisfaction measures such as average waiting time and average queue length. With a 25% increase in photovoltaic panels, the blackout period decreases by 2.12%, while a 25% decrease in photovoltaic panels causes an increase of 2.18% in the blackout period. Utilizing the energy management system (EMS), the waiting time was reduced by 8%.

Thermoviscoelastic modelling of highly reactive thermoset resins for liquid moulding applications

This paper presents a methodology for developing thermo-mechanical properties model for highly reactive resins and its use for Resin Transfer Moulding process (RTM) modelling and prediction of residual stresses. Cure Hardening Instantaneously Linear Elastic (CHILE), and Thermoviscoelastic (TVE) models, were implemented to analyze the mechanical behaviour of the resin. Simulation of the RTM process was developed and applied to a representative curved plate geometry. An integral approach was considered where the degree of cure gradient, generated during the filling stage due to the reactivity of the resin, was implemented as initial condition of the stress-deformation process simulation. The validation process included fabricating experimental parts with the representative geometry. The degree of cure variation of highly reactive thermosets during injection caused a significant effect on the final shape of the parts. These effects were captured by the simulations, where the TVE model showed a more accurate prediction of the part distortion.

A novel xanthan-chitosan based peroxydisulfate controlled-release material for in-situ sustained degradation of tetracycline: Performance, mechanism and application

In situ chemical oxidation (ISCO) is a promising technology to remove the organic pollutants from groundwater on site. In order to ensure the sustainable and long-term treatment performance, the oxidant controlled-release materials (CRMs) are required. In this study, a novel environmentally friendly and low-cost peroxydisulfate CRM (XG/CS-PDS) was synthesized via the mixing and mold method with xanthan, polyvinyl alcohol and chitosan as the binders for the first time. The XG/CS-PDS possessed significantly stable controlled-release properties under different pH and co-anions. 80% PDS in the CRMs could continuously and slowly release within 50 days. The simulation of PDS controlled-release process indicated that the process was controlled by the non-Fickian diffusion. Then the as-prepared CRM was applied to remove tetracycline (TC) from synthesized groundwater with nanoscale zero-valent iron (nZVI) as the PDS activator. The XG/CS-PDS/nZVI system could achieve 100% removal efficiency at different initial TC concentrations (5‐40 mg/L) and had a good anti-interference ability to pH, inorganic co-anions and humic acid. The quenching experiments and electron spin resonance (ESR) revealed that SO4•-, •OH and 1O2 were the main reactive oxygen species (ROS) for the degradation of TC, while SO4•- played the more dominant role. The intermediates and possible degradation pathway of TC were proposed and the toxicity of TC and its intermediates were evaluated. This study provided a new conception for the design of controlled-release material and theoretical strategies for in-situ remediation of groundwater.

Design, simulation and investigation of the tri-generation process of fresh water, power and biogas using solar thermal energy and sewage sludge

To address the high demand for energy and fresh water, the effect of global warming and pollution generated by fossil fuels consumption needs to be minimized and replaced with alternative renewable energy. Hybrid renewable energy systems using solar energy and biomass will become more crucial. In this study a new design for continuous production process of power, biogas and fresh water is proposed. The challenge of the present research is to use different sources for energy production from renewable resources simultaneously including solar and biomass to produce power, fuel and fresh water, using 4-stage Multi Effect Distillation (MED) for desalination of seawater. To cope with constant and stable energy production required for renewable solar power plants at night, due to lack of sunlight, co-consumption of hybrid solar and biomass feedstocks is considered in this paper. The associated challenge is discussed by the assessments of Solar Heat transfer fluid (SHTF), Sewage sludge flowrates, biogas production, output waste stream of gasification reactor, power and fresh water production using ASPEN Plus software. The sensitivity analysis was performed to demonstrate the viability of the designed process and the resulting products. The gasification process produced 76.8586 tonh syngas (CO and H2). Also, 34.547 MW of power with about 783 m3h of fresh water were produced with the overall exergy efficiency of 35.51%, 52.260 MW exergy destruction and desalination efficiency of 52%. Only 25 MW of solar power were necessary due to proper energy recovery throughout the entire process. The productivity of fresh water production was 50%. 38908 m2 of Parabolic trough collector (PTC) was needed to supply required solar energy during day light hours. It was revealed that the hybridization of solar & biomass energy resources for producing power, fresh water, and biogas could be a sustainable approach to cope with the increasing demand for fresh water, electricity, and fuel.

Fast approximation of fiber reinforced injection molding processes using eikonal equations and machine learning

Injection molding is a popular production process for short fiber reinforced components. The mechanical properties of such components depend on process-induced fiber orientations which are commonly predicted via numerical simulations. However, high computational costs prevent process simulations from being used in iterative procedures, such as topology optimization or finding optimal injection locations. We propose a fast approximation method that extracts nodal features and train a regression model to predict fill states, cooling times, volumetric shrinkage, and fiber orientations. The features are determined by solving eikonal equations with a fast iterative method and computing spatial moments to characterize node-adjacent material distributions. Subsequently, we use these features to train feed forward neural networks and gradient boosted regression trees with simulation data of a large dataset of geometries. This approach is significantly faster than conventional methods, providing 20x speed-up for single simulations and more than 200x speed-up in gate location optimization. It generalizes to arbitrary geometries and injection locations.

Research on Stamping Formability and Process Simulation of AZ31 Magnesium Alloy Based on Deep Learning

To study the formability of AZ31 magnesium alloy under different temperature conditions and improve the forming quality of its parts, this paper aims to solve the minimization problem of pursuing the “maximum thinning rate”. This study obtained the mechanical properties of AZ31 magnesium alloy at 25 °C, 150 °C, 250 °C, and 350 °C through uniaxial tensile tests and explored the stamping formability of AZ31 magnesium alloy mobile phone cases using the finite element inverse calculation method (MSTEP). Based on the hot stamping of AZ31 magnesium alloy at 250 °C, the Monte Carlo Simulation (MCS) method was used to randomly sample the blank holder force, stamping speed, friction coefficient, and resistance coefficient, and then simulate to obtain the corresponding “maximum thinning rate”. In addition, a hybrid prediction model optimized by Genetic Algorithm (GA) and Dung Beetle Optimization (DBO) based on the Back Propagation Neural Network (BPNN) - Random Forest (RF) was used to explore the nonlinear relationship between blank holder force, stamping speed, friction coefficient, resistance coefficient, and the “maximum thinning rate”. The results show that the plasticity of AZ31 magnesium alloy enhances with the temperature increase, especially exhibiting the best stamping formability at 250 °C. Feature importance indicates that the resistance coefficient and stamping speed have a greater impact on the maximum thinning rate, while the influence of blank holder force and friction coefficient is relatively smaller. Mean Square Error (MSE) shows that the predictive capability of the hybrid model is significantly superior to that of the single BP neural network and Random Forest model, displaying optimal efficiency. The predicted maximum thinning rate by the model is 15.39%, with an error rate of only 0.32% compared to the DYNAFORM finite element software simulation results, confirming the correctness, accuracy, and effectiveness of the prediction model. Overall, this study provides practical optimization strategies for metal stamping forming processes, with significant practical application value.

Thermodynamic modeling and process evaluation of advanced ionic liquid-based solvents for CO2/CH4 separation

Carbon capture technology is a prospective strategy to address the increasing concentration of CO2 in the atmosphere, with the core challenge of developing new cost-effective processes. In this work, the energy analysis and economic evaluation were conducted based on rigorous thermodynamic models and the process simulation results of a novel chemical absorption-dominated hybrid solvent which consists of functional ionic liquid of choline triazole ([Cho][Triz]) and sulfolane (TMS) to separate CO2 from shale gas. The solubility of CO2 and CH4 in different solvents were calculated using phase equilibrium model including the NRTL activity coefficient equation, the RK equation of state, chemical reaction equilibrium equations, and the mass balance equation. The physical properties of the ionic liquid-based solvent systems were calculated with empirical equations which was corrected using experimental data. The results obtained from the thermodynamic models exhibited good agreement with experimental data. Subsequently, the established models and the parameters obtained were embedded into Aspen Plus for further analysis. The total CO2 capture energy consumption of 1.65 GJ·t−1 CO2 and the cost of 48.07 $·t−1 CO2 were achieved using the new solvent when the mass fraction of IL was 60 wt%. Compared with the commercial 30 wt% MDEA carbon capture process, it reduced the energy consumption and economic cost of 64.16 % and 45.59 %, respectively.

Effect of flip-chip ball grid array structure on capillary underfill flow

During the capillary underfill (CUF) process in flip-chip packaging, issues like incomplete filling, voids, wire sweeping, and overflow can impact product reliability and the relatively long filling time required. Therefore, finding ways to improve filling efficiency is a critical challenge. Conducting experiments to address these issues can be costly. As a result, the industry often relies on simulation software to simulate the actual manufacturing process, predict potential problems, compare results with experimental data, and analyze ways to improve the process. This approach helps enhance the package yield and further improves production efficiency.This study investigates the capillary underfill (CUF) process through simulation. Firstly, the viscosity and reaction kinetics of the underfill material are measured to establish the necessary data for the simulation. Then, Moldex3D, a mold flow simulation software, is used to create the model, generate the mesh, and perform the process simulation analysis. The effects of material properties and process parameters on the capillary underfill process are considered. The simulation results are compared with experimental data to validate the feasibility of the simulation. Subsequently, different flip-chip package structures and CUF process parameters are analyzed through simulation to explore the impact of these parameters on the flow pattern and filling efficiency of the adhesive material during the filling process.According to the simulation results, the filling speed is faster when the bump pitch and gap height are larger. However, the required filling time still has a considerable relationship with the volume of the filling region. Dispensing from the side with lower bump density can shorten the filling time when the bump distribution is uneven.

Optimization of Energy Consumption in Formaldehyde Production Process Using Reboiled Absorption Process

Formaldehyde is a crucial chemical building block in various industries, and its production often involves energy-intensive processes. This study focuses on optimizing energy consumption in formaldehyde production, explicitly employing the reboiled absorption process with a production capacity of 27,000 tons per year. The objective of this article is to develop a more energy-efficient process of formaldehyde synthesis by addition or refrigerant cycle stream to preserve the energy, reducing energy consumption and improving the sustainability of the process. The reboiled absorption process involves the absorption of formaldehyde gas into a liquid absorbent, followed by reboiling to release the absorbed formaldehyde. A comprehensive analysis of the entire production system compares unmodified and modified process simulations, heat integration, and energy analysis. Beside the energy consumption of the process, the number of stages within the absorption process contributes to the product mass flow rate of the overall process by increasing the surface area which mass transfer can occur. However, adding too many stages to the process may negatively impact the energy efficiency of the process. Therefore, optimizing energy consumption and absorption processes in formaldehyde production is essential to improve the sustainability of the process and increase the overall profitability of the production process. The results show that the proposed method dramatically improves the sustainability of CH2O synthesis by reducing overall energy consumption and emissions by 93.978%, reducing energy consumption from 153,735,360.4 kJ/h to 9,256,646.618 kJ/h. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

An improved canopy interception scheme into biogeochemical model for precise simulation of carbon and water fluxes in subtropical coniferous forest

The process-based Biome-BGC (Biome Biogeochemical Cycles) model is widely used to simulate the fluxes of carbon and water of terrestrial ecosystems. While exhibiting excellent performance in simulating carbon cycle processes, this model provides a relatively simple simulation of the water cycle processes, particularly in canopy interception, which may result in significant errors in evapotranspiration (ET) and soil moisture estimations. This study initially refined the temporal scale of the original Biome-BGC model from a daily scale to an hourly scale, and then incorporated a theoretical interception module into the hourly-scale model for correcting canopy interception algorithm, to enhance the accuracy of hydrological processes simulations. The modified model was tested using the half-hourly observed meteorological and eddy covariance flux data at the Qianyanzhou subtropical coniferous forest site. In comparison with the original daily-scale model, simulations from the hourly-scale model showed better agreement with measurements at Qianyanzhou forest site, with 30.61 % and 40.92 % lower annual average gross primary productivity (GPP) and ET simulations. Although the accuracy of GPP and ET simulations did not show a significant improvement after canopy interception correction, it notably enhanced the accuracy of canopy interception and soil moisture simulations. The canopy interception rates were 55.8 % and 44.1 % for the original daily-scale and hourly-scale model, obviously overstimated compared with canopy correction simulation (31.1 %), leading to a significant underestimation of runoff. The canopy interception and runoff simulations after interception correction were proven relatively more reasonable. Additionally, the coefficient of determination (R2) for measured vs simulated soil water content (SWC) were 0.38, 0.51 and 0.60 for the original daily-scale, hourly-scale, and canopy interception correction simulations respectively, with a notable improvement in accuracy. This study suggested that improvements in temporal scale and canopy interception for process-based biogeochemical model can provide a better understanding of the carbon and water exchanges in response to instantaneous meteorological conditions and support improved water resource management.

A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes

As the development of green energy and smart industries progresses, concerted efforts are being directed towards the transformation of coking industries. Effective management of heat transfer during the coking process is essential to fortify energy consumption management and augment the quality of coke produced. However, the process during cokemaking is a complex thermochemical conversion process where even minor deviations in operating conditions can lead to instability. Additionally, due to the environmental and chemical conditions inside the coke oven, temperature measurements during operation are difficult. As the fields of Industry 4.0 continue to evolve, numerous industries have begun to leverage intelligent strategies to enhance advanced process management. The digital twin (DG) technique stands as a pivotal technology in the realm of industrial transformation, paving the way for a smarter future. However, given the inherent difficulty in real-time data collection for process operations, the challenge of achieving synchronization becomes increasingly prevalent in the development of DG for the smart coking process. As a result, predicting changes in coal temperature during the cokemaking process is crucial. Currently, computer technology allows for the simulation of complex chemical processes. While some numerical models describe the cokemaking process, these models are limited by the time-consuming characteristic and computing resources. To address this issue, this study proposed a Machine Learning (ML) and Computational Fluid Dynamics (CFD) hybrid model for real-time prediction of heat transfer during the cokemaking process. A CFD model was built based on an industrial-scale coke oven, with cokemaking process data generated through CFD simulation serving as input for the ML models. This study used a total of nine ML models, including ensemble and deep learning models. The hyperparameters of each model were optimized by Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and JAYA algorithms to identify the optimal model structure. Both simulation and empirical experimentation using a coke oven, coupled with industrial data verification, have substantiated the superiority of the proposed model. It simulates the heat transfer during the coking process with high accuracy and efficiency, thereby demonstrating its capacity to significantly reduce energy consumption within the coking industry.