Kinetic Model For Pyrolysis Research Articles

Fe2O3, Al2O3, or sludge ash-catalyzed pyrolysis of typical 3D printing waste toward tackling plastic pollution

Catalytic pyrolysis offers a potential solution to tackling plastic pollution by transforming plastic waste into valuable chemicals. This study explored the catalytic pyrolysis of 3D printing waste (3DPW), specifically focusing on photosensitive resin waste (PRW) and polycaprolactone waste (PCLW), with Al2O3, Fe2O3, or sludge ash (SA) containing Fe/Al. The study revealed a synergistic effect between the catalyst and 3DPW, influencing the pyrolysis properties and kinetic models. The addition of Fe2O3 significantly accelerated the main degradation stages, promoting the releases of 2-Ethylacrolein (64.78 % from PRW) and 2-Oxepanone (16.45 % from PCLW), as well as decreasing the acidic products. The catalytic pyrolysis changed the valence state of Fe, with some Fe(III) shifting to Fe(II), accompanied by the release of CO2. The addition of Al2O3 or SA generated new gaseous products (e.g., 2.93 % 1,3-Pentadiene, 2-methyl-, (E)-) through volatile reforming. The joint optimization of the multi-response artificial neural network revealed PCLW/Fe2O3 between 325–399 °C (D = 0.669) as the optimal operation for achieving both minimal remaining mass and maximum decomposition rate. These findings offer actionable insights into the catalytic pyrolysis of 3DPW, promoting its efficient treatment and clean reutilization.

Pyrolysis kinetic analysis and model constructions of different ranks of coal and validation by GA–BP neural networks

In the current steel industry, environmental pressures are mounting. If the reducing gases generated from coal pyrolysis can be utilized for reducing iron ore, the goal of low-carbon production with controllable costs can be achieved. However, the release of volatile content in coal has a significant impact on the pyrolysis process and final products. Therefore, in this study, the pyrolysis characteristics and gaseous products of four types of coal with different volatile matter content was investigated by thermogravimetry-mass spectrometry (TG-MS) technology. The results showed that coals with higher volatile matter content demonstrated stronger reactivity during pyrolysis. As the volatile matter content in coal increased, the starting temperature for releasing the reducing gases decreased, leading to a significant increase in the released gas volume. In addition, the three kinetic factors were calculated by isoconversional method, distribution of the activation energy model (DAEM) and Coats-Redfern (CR) method respectively, and the pyrolysis reaction mechanism function suitable for all coal types was reconstructed based on common solid reaction models. Finally, a predictive model based on a GA-improved back-propagation neural network (BPNN) was developed, which useing 21 input parameters to predict the TG curves, activation energy (E), lnA and mechanism function (fα) and achieving R2 values above 0.95. This study contributes to a comprehensive understanding of the pyrolysis mechanism of coals with different volatile matter content, providing scientific guidance for effective utilization.

Suppressive effects of potassium salt modified dry water material on hydrogen/methane mixture explosion

As a new type of energy, hydrogen-enriched methane mixed fuel has a high risk of deflagration in the application process. In order to better understand the deflagration and inhibition characteristics of hydrogen-enriched methane fuel, the effect of potassium salt modified dry water material (DW) on the deflagration characteristics of hydrogen-enriched methane fuel was studied by 20 L spherical explosive apparatus, combined with pyrolysis characteristics and kinetic model. The results show that when the addition of different modifier concentrations, it can be divided into three types: small curvature, medium curvature and large curvature according to the change of pressure rising rate. With the increase of the proportion of potassium salt modifiers, the number of areas with small curvature is gradually increasing, the number of large curvature is gradually decreasing, and the number of medium curvature is almost unchanged. When the proportion of potassium salt modifier is 15% and ϕ ≤ 0.7, all enter the area below the medium curvature. When the addition of different modifiers, 15%KCl-DW has the best inhibition effect on Pmax and (dP/dt) max, while CH3COOK-DW and K2CO3-DW have better inhibition effect on Δt2 and Δt2. In general, when the hydrogen addition ratio is less than 70%, the addition of inhibitors can significantly reduce the explosion intensity. In terms of inhibition mechanism, we established the decomposition model of potassium salt modified dry water and found that potassium salt modified dry water will produce a large amount of gaseous K and KOH in the explosion system. And gaseous K and KOH will catalyze each other and combine with H and OH to form stable H2O, which significantly reduces the mass fraction of H and OH radicals in the chain reaction. The results of this paper provide a theoretical basis for the safe use of hydrogen-enriched methane fuel and the research and development of explosion emergency prevention and control technology.

Multiscale Modeling of Plastic Pyrolysis with a Neural Network-Inspired Pyrolysis Kinetic Model and Coarse-Grained DEM-CFD

Multiscale Modeling of Plastic Pyrolysis with a Neural Network-Inspired Pyrolysis Kinetic Model and Coarse-Grained DEM-CFD

Thermal response of timber connections using densified wood dowels under fire

A new type of timber connection using densified wood dowels is being developed and tested. The procedure involves inserting these densified dowels into pre-drilled holes. As this connection technique is in its early stages, a unique design approach is necessary, considering the impact of temperature variations. The primary goal is to characterize the thermal behaviour of these connections under elevated temperatures. The study employs an experimental approach, complemented by numerical analysis, innovatively applying kinetic models, commonly used for investigating heat-related biomass characteristics, to wood. The method requires the use of thermogravimetric analysis to identify the kinetic parameters. The proposed pyrolysis kinetic model has been implemented in the Abaqus/Implicit code via a user subroutine UMATHT. The study concludes that using kinetic models enhances accuracy by considering mass loss, a key factor influencing thermal properties. Simulation successfully replicates temperature distribution and charred layer thickness, crucial for designing timber structures.

Impact of high-temperature biomass pyrolysis on biochar formation and composition

The development and utilization of biomass play a vital role in reducing fossil fuel dependency and mitigating greenhouse gas emissions. High-temperature pyrolysis provides a promising route for converting biomass into valuable products without tar formation. Kinetic models are essential for understanding biomass pyrolysis processes, aiding reactor design and optimization. In this study, rice husk (RH) and corn straw (CS) are selected, which exhibit significant differences in ash content but are widely present. Pyrolysis is performed using a thermogravimetric analyzer coupled with a mass spectrometer (TGA-MS). The results show a rapid decrease in solid residue oxygen content at elevated temperatures, which stabilized after reaching 900°C, accounting for about 8–10%. MS quantification indicates increased release of H2O and CO during this stage. Fourier transform infrared spectroscopy (FTIR) analysis on the biochar unveils that this phenomenon is attributed to the stretching vibration of C-O bonds and the conversion of -OH groups. The remaining oxygen primarily exists as carbonyl and carboxyl groups. Subsequently, the CRECK-S-B biomass pyrolysis kinetic model is updated, specifically targeting the transformation mechanism of oxygen-containing solids at high temperatures to improve the prediction of biochar yield and elemental composition. The relative error of oxygen content prediction is less than 10%. The accuracy of the model is validated through experimental data and an extensive literature database, leading to the establishment of a comprehensive database. The updated model demonstrates significantly enhanced prediction accuracy for pyrolysis temperatures above 800°C, expanding its applicability range. Moreover, it achieves an accuracy rate exceeding 80% for char yield and elemental content in the temperature range of 200–1000°C, including torrefaction conditions. It provides a theoretical foundation for the effective utilization of high-temperature biochar, offers a novel insight into biomass thermochemical conversion, and contributes to the sustainable development of biomass energy.

Multi-objective genetic algorithm approach for enhanced cumulative hydrogen and methane-rich syngas emission through co-pyrolysis of de-oiled microalgae and coal blending

Towards establishing low-carbon bio-economy, the energy-rich syngas is considered a global energy carrier. Targeting hydrogen as a promising advanced fuel, the significance of methane also increases due to its direct conversion capability into hydrogen. The current study aims to use a co-pyrolysis-based valorization of de-oiled microalgae and low-rank coal blend to generate H2 and CH4 rich syngas. Pyrolysis kinetic models, Kissinger-Akahira-Sunose (KAS) and Starink (STK) are used to evaluate apparent activation energy (Ea). The gradual addition of microalgae (0⎼100%) in coal reduces Ea from 189.11⎼55.87 kJ/mol and 180.16⎼54.61 kJ/mol by KAS and STK method, respectively. The maximum hymethane carrying ratio is observed 2.51 and 3.51 at optimized conditions of response surface methodology (RSM) and artificial neural network-based multi-objective genetic algorithm (ANN-MOGA), respectively. Maximum H2 (54.5 %) in the syngas is observed at mid pyrolysis stage (451 °C) using ANN-MOGA optimized conditions (blending ratio - 42.25 % and heating rate-13.8 °C/min). This study highlights the advantage of ANN-MOGA optimization over statistical based optimization. Hence, incorporating of the evolutionary algorithm as integrated ANN-MOGA optimization could be an efficient way for hymethane rich syngas emission in co-pyrolysis approach to gain carbon-neutral energy with zero waste discharge.

Kinetic reaction mechanism of lignocellulosic biomass oxidative pyrolysis based on combined kinetics

The kinetics knowledge of lignocellulosic biomass decomposition is essential to develop efficient thermochemical conversion technology. However, the simplification of reaction mechanisms in existing oxidative pyrolysis studies largely compromises the application of kinetic models. To explore more exact kinetic parameters and reaction mechanism of lignocellulosic biomass oxidative pyrolysis, an updated oxidative pyrolysis kinetic model (seven-step reaction combined kinetics model) coupled with an optimization algorithm is proposed. Based on a series of thermogravimetric experiments in an air atmosphere, the extra oxidative pyrolysis kinetic parameters are obtained by the Shuffled Complex Evolution method. The proposed kinetic model is validated based on the degradation process of each component (hemicellulose, cellulose, and lignin). Furthermore, the obtained kinetic parameters are applied to predict the oxidative pyrolysis behavior, and the predicted mass loss rate is in good agreement with the experimental data. Eventually, according to the key combined kinetics parameters, it is found that the oxidative pyrolysis mechanisms of hemicellulose, cellulose, and lignin correspond to the power law, nucleation & growth, and chemical reaction order, respectively, while the combustion of char corresponds to the reaction order mechanism.

A review on thermogravimetric analysis‐based analyses of the pyrolysis kinetics of oil shale and coal

AbstractSolid fossil fuels still support the development of the energy and chemical industry. The clean and efficient utilization of solid fossil fuels can effectively alleviate the problems of energy shortage and environmental pollution. An in‐depth understanding of the kinetic process, models, and mechanism of pyrolysis of solid fossil fuels is also of great significance for the exploration and utilization technologies of petroleum resources. Thermogravimetric analysis is an effective method to study the pyrolysis process and kinetics with the advantages of high sensitivity, repeatability, and reliability for data acquisition. Researchers have conducted a lot of research on the thermogravimetric kinetics of solid fossil fuels in recent years, but there is a lack of systematic summary and comparison of kinetic methods, research progress, and trends. Therefore, a comprehensive study on pyrolysis kinetic principles, methods, and models of solid fossil fuels by thermogravimetric analysis is carried out in this paper. The influencing factors of pyrolysis kinetics of solid fossil fuels using thermogravimetry are elaborated and discussed in detail. This study also provides progress in recent years and prospects of thermogravimetric kinetics of solid fossil fuels. It is expected that this paper can provide certain technical support and theoretical guidance for the research on pyrolysis kinetics based on thermogravimetric analysis, and the exploration and development, clean, and efficient utilization of solid fossil fuels.

Global trends of pyrolysis research: a bibliometric analysis

Pyrolysis has become an interesting waste valorization method leading to an increasing number of research studies in this field in the last decade. The present study aims to provide a comprehensive knowledge map of scientific production in pyrolysis, discuss the current state of research, and identify the main research hotspots and trends in recent years. The systematic review, supported by analysis of countries and institutions, keyword co-occurrence analysis, analysis of keyword trends, journal analysis, and article impact, was carried out on 6234 journal articles from the Science Citation Index Expanded database of the Web of Science Core Collection. As a result, four main research hotspots were identified: 1) characterization techniques and pyrolysis kinetic models, 2) biochar production and its main applications, 3) bio-oil production and catalytic pyrolysis, and 4) co-pyrolysis, which has become a consolidated research hotspot since 2018. Additionally, the main challenges and opportunities for future research have been identified, such as 1) the development of multi-step kinetic models for studying complex wastes, 2) the integration of biochar into other valorization processes, such as anaerobic digestion, and 3) the development of catalytic hydropyrolysis for the valorization of organic waste. This bibliometric analysis provides a visualization of the current context and future trends in pyrolysis, facilitating future collaborative research and knowledge exchange.

Revisiting the reaction scheme of slow pyrolysis of woody biomass

Existing lumped kinetic models have limited accuracy in predicting the pyrolysis behavior of different materials. There is currently no universally accepted model capable of accurately predicting pyrolysis rates and final product yields for various materials under different experimental conditions. This study aims to address this limitation by assessing the sensitivity of a widely used wood pyrolysis kinetic model across multiple sets of experimental data. The analysis reveals that the existing model falls short in accurately predicting the yields of woody biomass at higher temperatures. To overcome this, two new kinetic models were proposed that incorporate additional reactions not accounted for in conventional models. These additional reactions have impact on the formation of secondary pyrolysis phases. The first proposed model introduces a term for secondary tar formation, which takes into account the production of more stable cracked, dehydrogenated, and deoxygenated tars that typically occur at elevated pyrolysis temperatures, possibly influenced by catalysts. The second proposed model expands on this concept by incorporating terms that represent the formation of secondary gases and chars arising from the primary chars. By including these additional reactions, the model enhances its accuracy and predictive capacity for determining the pyrolytic products of various types of woody biomass.

Three-dimensional coking simulation of endothermic hydrocarbon fuels in rectangular cooling channels

Coking deposition is a critical phenomenon for endothermal hydrocarbon fuel cooling that can significantly affect the performance of a regenerative cooling system. Because of the complicated interactions between fluid flow, heat transfer, fuel cracking, and precursor coking kinetics, previous numerical studies have been limited to simplified two-dimensional circular channels, which cannot reveal the actual spatial distribution with consideration of buoyancy effect in rectangular cooling channels. This work proposes a novel framework for shrinking motion with an O-type hybrid mesh, permitting the direct three-dimensional simulation of coke deposition in complex channels and the visualization of both the axial and circumferential deposition distributions. The concept is tested in a rectangular channel using n-decane as an example, combining a detailed pyrolysis kinetic model with the MC-II coking model, and predictive results have been obtained. Results indicate two locations with heavy deposition rates. The buoyancy effect is weakened due to the acceleration resulting from the reduced cross-sectional area of the channel by the coke layer. The coupling of the flow and pyrolysis is discussed in terms of the dimensionless Damköhler number. The maximum temperature after coking can be 138 K higher than the initial. However, the conversion of n-decane at outlet is decreased due to the reduced flow residence time. The decreased total heat sink per temperature increment and the higher pressure drop are also the penalties from coking. The new framework for the direct three-dimensional simulations of coking is significant for the comprehensive investigation of the efficiency of regenerative cooling.

Study on the pyrolysis characteristics and reaction mechanisms of WLED packaging materials

The waste light-emitting diode (WLED) is a new type waste electrical and electronic equipment (WEEE). The volume of waste WLEDs is huge and effective recycling and treatment have attracted widespread attention. However, it’s difficult to handle the WLED packaging materials in the recycling process because of the complex structure. Few studies have been conducted to analyze the efficient recycling of WLED packaging materials. In this study, the pyrolysis characteristics, pyrolysis kinetics and product composition of light-emitting diode (LED) packaging materials methyl phenyl silicon (MPS) were investigated using TG analysis, kinetic analysis, TG-FTIR analysis and Py-GC/MS analysis. Density functional theory (DFT) calculation was further combined to study the pyrolysis process of MPS. The TG analysis showed that the MPS pyrolysis was divided into two stages: 310–561 °C and 499–980 °C. The apparent activation energies of MPS pyrolysis were calculated by Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods to be 144.68–149.76 kJ·mol−1 (Stage I) and 95.99–105.47 kJ·mol−1 (Stage II), respectively. The pyrolysis kinetic reaction model of MPS was determined by the Coats-Redfern (CR) method: D1 for Stage I, C2 for Stage II. According to the TG-FTIR and Py-GC/MS analysis results, 8 major gaseous products in the MPS pyrolysis process were identified, including CO2, benzene, HCl, biphenyl, chlorobenzene, styrene, ethylbenzene and chlorodimethylphenylsilane. Finally, the formation mechanism of different pyrolysis products was determined by DFT calculation. The pyrolysis characteristics and pyrolysis mechanism of WLED packaging materials may be helpful for the optimization of WLEDs pyrolysis recycling process and the regulation of pyrolysis products.

Kinetic Analysis of Pyrolysis Reaction of Hydrogen-Containing Low Rank Coals Based on Thermogravimetric Method

Based on the combined technology of thermogravimetric analysis-mass spectrometry (TG-MS), the non isothermal kinetic of two low rank coals with high hydrogen content and the release characteristics of H2 in volatiles were explored. The effect of coal species and coal particle size on pyrolysis was studied. The CR (Coats-Redfern) method and the DAEM (Distributed activation energy model) method were used to construct the pyrolysis kinetic model by dividing temperature stages, and the results showed that the mechanisms of pyrolysis process of lower rank coals were mainly 3-D diffusion (G-B) and Growth of nucleation (Avrami-Erofeev, n = 1/3). The use of the DAEM method made up for the shortcomings of the CR method in calculating the activation energy, and the analysis of coal pyrolysis kinetics was more comprehensive. The higher the volatile content the coal contained, the lower activation energy the pyrolysis process would require. The larger the particle size of the coal was, the higher activation energy the pyrolysis process would need. The release law of H2 in low rank coal during the pyrolysis process was analyzed by mass spectrometry, and results were presented: the first peak occurs at 500 °C and the second at 750 °C, which was mainly generated by the condensation and dehydrogenation reactions of aromatic compounds.

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis.

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

Insights into the influence of methanolysis on the physicochemical structure variation and pyrolysis reactivity of wheat straw

Alkanolysis had become the promising approach for extracting organic compounds of lignocellulosic biomass, but its effect on the pyrolysis behaviors of biomass still received little attention. In this research, the influence of methanolysis pretreatment on the physicochemical structure variation, pyrolysis kinetics, and thermodynamic of wheat straw (WS) was systematically investigated using four multiple direct tools. C element content of pretreated WS greatly increased and O-containing functional groups was extracted, such as C-O bonds. Thermal decomposition behaviors of WS and pretreated WS were conducted by Thermogravimetric analyzer (TGA) at the heating rate of 10, 20, and 30 °C/min, respectively. Three model-free methods such as Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS), and Starink were applied to evaluate the pyrolysis kinetics and thermodynamics parameters of three samples. The average Ea value obtained using three model-free methods for R250 °C (340.84, 345.88, and 345.02 kJ/mol) were higher in comparison to WS (192.48, 191.15, and 192.39 kJ/mol) and R220 °C (175.05, 180.10, and 177.63 kJ/mol). The pyrolysis kinetic model of WS and R220 °C followed D3 or R3 (0.1 ≤α ≤ 0.7) and F2 diffusion model (α ≥ 0.7). D3 or R3 (0.1 ≤α ≤ 0.7) and F3 diffusion model (α ≥ 0.7) can accurately describe the kinetic model of R250 °C. Combined with thermodynamic data analysis of three samples, R250 °C required more external energy for pyrolysis process due to more existence of lignin. This research could provide the new understandings for the pyrolysis process of liquefaction residue of biomass.

Pyrolysis and combustion behavior study of PMMA waste from micro-scale to bench-scale experiments

With the increasing use of polymethyl methacrylate (PMMA), there is growing concern about its waste utilization. It is of great significance to study its pyrolysis and combustion behaviors, then a series of experiments were conducted from micro-scale (thermogravimetry, fourier transform infrared spectroscopy and mass spectrometry) to bench-scale (fire propagation apparatus and cone calorimeter). By the micro-scale experiments, the pyrolysis kinetic model was established and kinetic parameters were obtained by coupling the Shuffled Complex Evolution optimization algorithm. Moreover, based on the inferred pyrolysis gases and functional groups, the possible chemical reactions of PMMA pyrolysis from molecular structure were also put forward. By the bench-scale experiments, the pyrolysis and combustion behaviors were further studied by coupling the pyrolysis kinetics and mass/heat transfer process, including mass loss rate, heat release rate and CO2/CO production. The obtained pyrolysis and combustion characteristics by the multi-scale experiments would be helpful for the further energy utilization.

Thermal Analysis and Kinetic Modeling of Pyrolysis and Oxidation of Hydrochars

This study examines the kinetics of pyrolysis and oxidation of hydrochars through thermal analysis. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) techniques were used to investigate the decomposition profiles and develop two distributed activation energy models (DAEM) of hydrochars derived from the hydrothermal carbonization of grape seeds produced at different temperatures (180, 220, and 250 °C). Data were collected at 1, 3, and 10 °C/min between 30 and 700 °C. TGA data highlighted a decomposition profile similar to that of the raw biomass for hydrochars obtained at 180 and 220 °C (with a clear distinction between oil, cellulosic, hemicellulosic, and lignin-like compounds), while presenting a more stable profile for the 250 °C hydrochar. DSC showed a certain exothermic behavior during pyrolysis of hydrochars, an aspect also investigated through thermodynamic simulations in Aspen Plus. Regarding the DAEM, according to a Gaussian model, the severity of the treatment slightly affects kinetic parameters, with average activation energies between 193 and 220 kJ/mol. Meanwhile, the Miura–Maki model highlights the distributions of the activation energy and the pre-exponential factor during the decomposition.

Autonomous kinetic modeling of biomass pyrolysis using chemical reaction neural networks

Modeling the burning processes of biomass such as wood, grass, and crops is crucial for the modeling and prediction of wildland and urban fire behavior. Despite its importance, the burning of solid fuels remains poorly understood, which can be partly attributed to the unknown chemical kinetics of most solid fuels. Most available kinetic models were built upon expert knowledge, which requires chemical insights and years of experience. This work presents a framework for autonomously discovering biomass pyrolysis kinetic models from thermogravimetric analyzer (TGA) experimental data using the recently developed chemical reaction neural networks (CRNN). The approach incorporated the CRNN model into the framework of neural ordinary differential equations to predict the residual mass in TGA data. In addition to the flexibility of neural-network-based models, the learned CRNN model is interpretable, by incorporating the fundamental physics laws, such as the law of mass action and Arrhenius law, into the neural network structure. The learned CRNN model can then be translated into the classical forms of biomass chemical kinetic models, which facilitates the extraction of chemical insights and the integration of the kinetic model into large-scale fire simulations. We demonstrated the effectiveness of the framework in predicting the pyrolysis and oxidation of cellulose. This successful demonstration opens the possibility of rapid and autonomous chemical kinetic modeling of solid fuels, such as wildfire fuels and industrial polymers.

Techno-economic, environmental, and heat integration of palm empty fruit bunch upgrading for power generation

The palm empty fruit bunch (PEFB) is a residue from oil pressing. As a renewable energy source, it is expected to achieve high-yield bio-oils, low gas content, and low char content. In this work, a comprehensive pyrolysis process model, bio-oil upgrading plant, and electric power generation were simulated simultaneously. This work applied the pyrolysis kinetic reaction model at 500 °C based on 1,000 tons/day of PEFB. The results showed that suitable validation with the existing published work, in that 1 kg/s of PEFB could produce 0.21 kg/s of gas, 0.67 kg/s of bio-oil, and 0.11 kg/s char. Implementing a heat exchanger network (HEN) improves the minimum capital cost implication and maximum heat recovery compared to the non-heat exchanger network (non-HEN) design. Therefore, HEN implementation shows a cost reduction of 57.46%, with a feasibility study performed by estimating the equipment size and cost. The economic assumptions are estimated to be 10% of the annual discount rate for 20 years. According to an economic analysis of the HEN, the net present value, internal rate of return and payback duration are $20,086,830.26, 28.40%, and 5.62 years, respectively. In comparison, the non-HEN design shows $12,966,333.26, 27.55%, and 5.77 years, respectively. Furthermore, the environmental-economic analysis shows that the non-HEN design and HEN design indicate global warming at midpoints of 281,677 and 263,330-ton carbon dioxide equivalent (CO2 eq.)/year. The non-HEN design has a higher midpoint and endpoint than the HEN design.