Academic literature on the topic 'Industrial Chemistry - Computational Study - Molecules and Processes'
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Journal articles on the topic "Industrial Chemistry - Computational Study - Molecules and Processes"
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Oyewola, David Opeoluwa, Emmanuel Gbenga Dada, Onyeka Emebo, and Olugbenga Oluseun Oluwagbemi. "Using Deep 1D Convolutional Grated Recurrent Unit Neural Network to Optimize Quantum Molecular Properties and Predict Intramolecular Coupling Constants of Molecules of Potential Health Medications and Other Generic Molecules." Applied Sciences 12, no. 14 (July 18, 2022): 7228. http://dx.doi.org/10.3390/app12147228.
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A molecule is the smallest particle in a chemical element or compound that possesses the element or compound’s chemical characteristics. There are numerous challenges associated with the development of molecular simulations of fluid characteristics for industrial purposes. Fluid characteristics for industrial purposes find applications in the development of various liquid household products, such as liquid detergents, drinks, beverages, and liquid health medications, amongst others. Predicting the molecular properties of liquid pharmaceuticals or therapies to address health concerns is one of the greatest difficulties in drug development. Computational tools for precise prediction can help speed up and lower the cost of identifying new medications. A one-dimensional deep convolutional gated recurrent neural network (1D-CNN-GRU) was used in this study to offer a novel forecasting model for molecular property prediction of liquids or fluids. The signal data from molecular properties were pre-processed and normalized. A 1D convolutional neural network (1D-CNN) was then built to extract the characteristics of the normalized molecular property of the sequence data. Furthermore, gated recurrent unit (GRU) layers processed the extracted features to extract temporal features. The output features were then passed through several fully-connected layers for final prediction. For both training and validation, we used molecular properties obtained from the Kaggle database. The proposed method achieved a better prediction accuracy, with values of 0.0230, 0.1517, and 0.0693, respectively, in terms of the mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE).
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Lanero, Francesco, Bianca Maria Bresolin, Anna Scettri, Marco Nogarole, Elisabetta Schievano, Stefano Mammi, Giacomo Saielli, et al. "Activation of Vegetable Oils by Reaction with Maleic Anhydride as a Renewable Source in Chemical Processes: New Experimental and Computational NMR Evidence." Molecules 27, no. 23 (November 23, 2022): 8142. http://dx.doi.org/10.3390/molecules27238142.
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Vegetable oils are bio−based and sustainable starting materials that can be used to develop chemicals for industrial processes. In this study, the functionalization of three vegetable oils (grape, hemp, and linseed) with maleic anhydride was carried out either by conventional heating or microwave activation to obtain products that, after further reactions, can enhance the water dispersion of oils for industrial applications. To identify the most abundant derivatives formed, trans-3-octene, methyl oleate, and ethyl linoleate were reacted as reference systems. A detailed NMR study, supported by computational evidence, allowed for the identification of the species formed in the reaction of trans-3-octene with maleic anhydride. The signals in the 1H NMR spectra of the alkenyl succinic anhydride (ASA) moieties bound to the organic chains were clearly identified. The reactions achieved by conventional heating were carried out for 5 h at 200 °C, resulting in similar or lower amounts of ASA units/g of oil with respect to the reactions performed by microwave activation, which, however, induced a higher viscosity of the samples.
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Ang, Dale L., Mubasher Zahir Hoque, Md Abir Hossain, Gea Guerriero, Roberto Berni, Jean-Francois Hausman, Saleem A. Bokhari, Wallace J. Bridge, and Khawar Sohail Siddiqui. "Computational Analysis of Thermal Adaptation in Extremophilic Chitinases: The Achilles’ Heel in Protein Structure and Industrial Utilization." Molecules 26, no. 3 (January 29, 2021): 707. http://dx.doi.org/10.3390/molecules26030707.
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Understanding protein stability is critical for the application of enzymes in biotechnological processes. The structural basis for the stability of thermally adapted chitinases has not yet been examined. In this study, the amino acid sequences and X-ray structures of psychrophilic, mesophilic, and hyperthermophilic chitinases were analyzed using computational and molecular dynamics (MD) simulation methods. From the findings, the key features associated with higher stability in mesophilic and thermophilic chitinases were fewer and/or shorter loops, oligomerization, and less flexible surface regions. No consistent trends were observed between stability and amino acid composition, structural features, or electrostatic interactions. Instead, unique elements affecting stability were identified in different chitinases. Notably, hyperthermostable chitinase had a much shorter surface loop compared to psychrophilic and mesophilic homologs, implying that the extended floppy surface region in cold-adapted and mesophilic chitinases may have acted as a “weak link” from where unfolding was initiated. MD simulations confirmed that the prevalence and flexibility of the loops adjacent to the active site were greater in low-temperature-adapted chitinases and may have led to the occlusion of the active site at higher temperatures compared to their thermostable homologs. Following this, loop “hot spots” for stabilizing and destabilizing mutations were also identified. This information is not only useful for the elucidation of the structure–stability relationship, but will be crucial for designing and engineering chitinases to have enhanced thermoactivity and to withstand harsh industrial processing conditions
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Cruz, Guillermo, Javier Acosta, Jose Miguel Mancheño, Jon Del Arco, and Jesús Fernández-Lucas. "Rational Design of a Thermostable 2′-Deoxyribosyltransferase for Nelarabine Production by Prediction of Disulfide Bond Engineering Sites." International Journal of Molecular Sciences 23, no. 19 (October 5, 2022): 11806. http://dx.doi.org/10.3390/ijms231911806.
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One of the major drawbacks of the industrial implementation of enzymatic processes is the low operational stability of the enzymes under tough industrial conditions. In this respect, the use of thermostable enzymes in the industry is gaining ground during the last decades. Herein, we report a structure-guided approach for the development of novel and thermostable 2′-deoxyribosyltransferases (NDTs) based on the computational design of disulfide bonds on hot spot positions. To this end, a small library of NDT variants from Lactobacillus delbrueckii (LdNDT) with introduced cysteine pairs was created. Among them, LdNDTS104C (100% retained activity) was chosen as the most thermostable variant, displaying a six- and two-fold enhanced long-term stability when stored at 55 °C (t1/255 °C ≈ 24 h) and 60 °C (t1/260 °C ≈ 4 h), respectively. Moreover, the biochemical characterization revealed that LdNDTS104C showed >60% relative activity across a broad range of temperature (30–90 °C) and pH (5–7). Finally, to study the potential application of LdNDTS104C as an industrial catalyst, the enzymatic synthesis of nelarabine was successfully carried out under different substrate conditions (1:1 and 3:1) at different reaction times. Under these experimental conditions, the production of nelarabine was increased up to 2.8-fold (72% conversion) compared with wild-type LdNDT.
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Pilon, Alan Cesar, Marcelo Del Grande, Maíra R. S. Silvério, Ricardo R. Silva, Lorena C. Albernaz, Paulo Cézar Vieira, João Luis Callegari Lopes, Laila S. Espindola, and Norberto Peporine Lopes. "Combination of GC-MS Molecular Networking and Larvicidal Effect against Aedes aegypti for the Discovery of Bioactive Substances in Commercial Essential Oils." Molecules 27, no. 5 (February 28, 2022): 1588. http://dx.doi.org/10.3390/molecules27051588.
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Dengue is a neglected disease, present mainly in tropical countries, with more than 5.2 million cases reported in 2019. Vector control remains the most effective protective measure against dengue and other arboviruses. Synthetic insecticides based on organophosphates, pyrethroids, carbamates, neonicotinoids and oxadiazines are unattractive due to their high degree of toxicity to humans, animals and the environment. Conversely, natural-product-based larvicides/insecticides, such as essential oils, present high efficiency, low environmental toxicity and can be easily scaled up for industrial processes. However, essential oils are highly complex and require modern analytical and computational approaches to streamline the identification of bioactive substances. This study combined the GC-MS spectral similarity network approach with larvicidal assays as a new strategy for the discovery of potential bioactive substances in complex biological samples, enabling the systematic and simultaneous annotation of substances in 20 essential oils through LC50 larvicidal assays. This strategy allowed rapid intuitive discovery of distribution patterns between families and metabolic classes in clusters, and the prediction of larvicidal properties of acyclic monoterpene derivatives, including citral, neral, citronellal and citronellol, and their acetate forms (LC50 < 50 µg/mL).
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Toghan, Arafat, Ahmed Fawzy, Areej Al Bahir, Nada Alqarni, Moustafa M. S. Sanad, Mohamed Khairy, Abbas I. Alakhras, and Ahmed A. Farag. "Computational Foretelling and Experimental Implementation of the Performance of Polyacrylic Acid and Polyacrylamide Polymers as Eco-Friendly Corrosion Inhibitors for Copper in Nitric Acid." Polymers 14, no. 22 (November 8, 2022): 4802. http://dx.doi.org/10.3390/polym14224802.
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Copper is primarily used in many industrial processes, but like many other metals, it suffers from corrosion damage. Polymers are not only one of the effective corrosion inhibitors but also are environmentally friendly agents in doing so. Hence, in this paper, the efficacy of two polyelectrolyte polymers, namely poly(acrylic acid) (PAA) and polyacrylamide (PAM), as corrosion inhibitors for copper in molar nitric acid medium was explored. Chemical, electrochemical, and microscopic tools were employed in this investigation. The weight-loss study revealed that the computed inhibition efficiencies (% IEs) of both PAA and PAM increased with their concentrations but diminished with increasing HNO3 concentration and temperature. The results revealed that, at similar concentrations, the values of % IEs of PAM are slightly higher than those recorded for PAA, where these values at 298 K reached 88% and 84% in the presence of a 250 mg/L of PAM and PAA, respectively. The prominent IE% values for the tested polymers are due to their strong adsorption on the Cu surface and follow the Langmuir adsorption isoform. Thermodynamic and kinetic parameters were also calculated and discussed. The kinetics of corrosion inhibition by PAA and PAM showed a negative first-order process. The results showed also that the used polymers played as mixed-kind inhibitors with anodic priority. The mechanisms of copper corrosion in nitric acid medium and its inhibition by the tested polymers were discussed. DFT calculations and molecular dynamic (MD) modelling were used to investigate the effect of PAA and PAM molecular configuration on their anti-corrosion behavior. The results indicated that the experimental and computational study are highly consistent.
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Villalba-Diez, Javier, and Xiaochen Zheng. "Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems." Sensors 20, no. 20 (October 16, 2020): 5856. http://dx.doi.org/10.3390/s20205856.
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The strategic design of organizations in an environment where complexity is constantly increasing, as in the cyber-physical systems typical of Industry 4.0, is a process full of uncertainties. Leaders are forced to make decisions that affect other organizational units without being sure that their decisions are the right ones. Previously to this work, genetic algorithms were able to calculate the state of alignment of industrial processes that were measured through certain key performance indicators (KPIs) to ensure that the leaders of the Industry 4.0 make decisions that are aligned with the strategic objectives of the organization. However, the computational cost of these algorithms increases exponentially with the number of KPIs. That is why this work makes use of the principles of quantum computing to present the strategic design of organizations from a novel point of view: Quantum Strategic Organizational Design (QSOD). The effectiveness of the application of these principles is shown with a real case study, in which the computing time is reduced from hundreds of hours to seconds. This has very powerful practical applications for industry leaders, since, with this new approach, they can potentially allow a better understanding of the complex processes underlying the strategic design of organizations and, above all, make decisions in real-time.
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Hu, Youxi, Chao Liu, Ming Zhang, Yu Jia, and Yuchun Xu. "A Novel Simulated Annealing-Based Hyper-Heuristic Algorithm for Stochastic Parallel Disassembly Line Balancing in Smart Remanufacturing." Sensors 23, no. 3 (February 2, 2023): 1652. http://dx.doi.org/10.3390/s23031652.
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Remanufacturing prolongs the life cycle and increases the residual value of various end-of-life (EoL) products. As an inevitable process in remanufacturing, disassembly plays an essential role in retrieving the high-value and useable components of EoL products. To disassemble massive quantities and multi-types of EoL products, disassembly lines are introduced to improve the cost-effectiveness and efficiency of the disassembly processes. In this context, disassembly line balancing problem (DLBP) becomes a critical challenge that determines the overall performance of disassembly lines. Currently, the DLBP is mostly studied in straight disassembly lines using single-objective optimization methods, which cannot represent the actual disassembly environment. Therefore, in this paper, we extend the mathematical model of the basic DLBP to stochastic parallel complete disassembly line balancing problem (DLBP-SP). A novel simulated annealing-based hyper-heuristic algorithm (HH) is proposed for multi-objective optimization of the DLBP-SP, considering the number of workstations, working load index, and profits. The feasibility, superiority, stability, and robustness of the proposed HH algorithm are validated through computational experiments, including a set of comparison experiments and a case study of gearboxes disassembly. To the best of our knowledge, this research is the first to introduce gearboxes as a case study in DLBP which enriches the research on disassembly of industrial equipment.
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Andrade, Ana, Kennedy Lopes, Bernardo Lima, and André Maitelli. "Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves." Sensors 21, no. 3 (January 27, 2021): 853. http://dx.doi.org/10.3390/s21030853.
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To satisfy the market, competition in the industrial sector aims for productivity and safety in industrial plant control systems. The appearance of a fault can compromise the system’s proper functioning process. Therefore, Fault Detection and Diagnosis (FDD) methods contribute to avoiding any undesired events, as there are techniques and methods that study the detection, isolation, identification and, consequently, fault diagnosis. In this work, a new methodology that uses faults emulation to obtain parameters similar to the Development and Application of Methods for Diagnosis of Actuators in Industrial Control Systems (DAMADICS) benchmark model will be developed. This methodology uses previous information from tests on sensors with and without faults to detect and classify the situation of the plant and, in the presence of faults, perform the diagnosis through a process of elimination in a hierarchical manner. In this way, the definition of residue signature is used as well as the creation of a decision tree. The whole process is carried out incorporating FDD techniques, through the Non-Linear Auto-Regressive Neural Network Model With Exogenous Inputs (NARX), in the diagnosis of the behavioral prediction of the signals to generate the residual values. Then, it is applied to the construction of the decision tree based on the most significant residue of a certain signal, enabling the process of acquisition and formation of the signature matrix. With the procedures in this article, it is possible to demonstrate a practical and systematic method of how to emulate faults for control valves and the possibility of carrying out an analysis of the data to acquire signatures of the fault behavior. Finally, simulations resulting from the most sensitized variables for the production of residuals that is generated by neural networks are presented, which are used to obtain signatures and isolate the flaws. The process proves to be efficient in computational time and makes it easy to present a fault diagnosis strategy that can be reproduced in other processes.
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Marinov, Marin B., Nikolay Nikolov, Slav Dimitrov, Todor Todorov, Yana Stoyanova, and Georgi T. Nikolov. "Linear Interval Approximation for Smart Sensors and IoT Devices." Sensors 22, no. 3 (January 26, 2022): 949. http://dx.doi.org/10.3390/s22030949.
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In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization of the number of points defining the characteristic, which in turn is related to the possibilities for using microcontrollers with limited energy and memory resources. In this context, the results from the study indicate that to overcome the problems arising from the resource constraints of smart devices, appropriate “lightweight” algorithms are needed that allow efficient connectivity and intelligent management of the measurement processes. The method has two benefits: first, low-cost microcontrollers could be used for hardware implementation of the industrial sensor devices; second, the optimal subdivision of the measurement range reduces the space in the memory of the microcontroller necessary for storage of the parameters of the linearized characteristic. Although the discussed computational examples are aimed at building adaptive approximations for temperature sensors, the algorithm can easily be extended to many other sensor types and can improve the performance of resource-constrained devices. For prescribed maximum approximation error, the inverse sensor characteristic is found directly in the linearized form. Further advantages of the proposed approach are: (i) the maximum error under linearization of the inverse sensor characteristic at all intervals, except in the general case of the last one, is the same; (ii) the approach allows non-uniform distribution of maximum approximation error, i.e., different maximum approximation errors could be assigned to particular intervals; (iii) the approach allows the application to the general type of differentiable sensor characteristics with piecewise concave/convex properties.
Book chapters on the topic "Industrial Chemistry - Computational Study - Molecules and Processes"
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Germain, Aurèle, Marta Corno, and Piero Ugliengo. "Computing Binding Energies of Interstellar Molecules by Semiempirical Quantum Methods: Comparison Between DFT and GFN2 on Crystalline Ice." In Computational Science and Its Applications – ICCSA 2021, 632–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86976-2_43.
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AbstractInterstellar Grains (IGs) spread in the Interstellar Medium (ISM) host a multitude of chemical reactions that could lead to the production of interstellar Complex Organic Molecules (iCOMs), relevant in the context of prebiotic chemistry. These IGs are composed of a silicate-based core covered by several layers of amorphous water ice, known as a grain mantle. Molecules from the ISM gas-phase can be adsorbed at the grain surfaces, diffuse and react to give iCOMs and ultimately desorbed back to the gas phase. Thus, the study of the Binding Energy (BE) of these molecules at the water ice grain surface is important to understand the molecular composition of the ISM and its evolution in time. In this paper, we propose to use a recently developed semiempirical quantum approach, named GFN-xTB, and more precisely the GFN2 method, to compute the BE of several molecular species at the crystalline water ice slab model. This method is very cheap in term of computing power and time and was already showed in a previous work to be very accurate with small water clusters. To support our proposition, we decided to use, as a benchmark, the recent work published by some of us in which a crystalline model of proton-ordered water ice (P-ice) was adopted to predict the BEs of 21 molecules relevant in the ISM. The relatively good results obtained confirm GFN2 as the method of choice to model adsorption processes occurring at the icy grains in the ISM. The only notable exception was for the CO molecule, in which both structure and BE are badly predicted by GFN2, a real pity due to the relevance of CO in astrochemistry.
Conference papers on the topic "Industrial Chemistry - Computational Study - Molecules and Processes"
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Mallouppas, George, Graham Goldin, Yongzhe Zhang, Piyush Thakre, and Jim Rogerson. "Validation of Chemistry Acceleration Techniques With an Industrial Gas Turbine." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90218.
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Abstract Two different numerical techniques for chemistry acceleration are examined with Large Eddy Simulation of a commercial swirl industrial gas turbine combustor operating at 3 bar. This work presents the results for SGT-100 Dry Low Emission (DLE) gas turbine provided by Siemens Industrial Turbomachinery Ltd. The related experimental study was performed at the German Aerospace Centre, DLR, Stuttgart, Germany. LES with detailed chemistry calculations is an attractive tool to study turbulent premixed flames in industrial gas turbine combustors, because it can help understand turbulence-chemistry interactions, detailed flame characteristics and pollutant formation. Detailed chemistry can capture kinetically dominated processes such as ignition, extinction and pollutant formation. However, computational resources required for such calculations are often prohibitive due to the computational costs of transporting and integration of a large number of species with a wide range of chemical time-scales. Chemistry acceleration techniques can substantially reduce run-time with ideally a small loss in accuracy. Therefore, the purpose of this work is to quantify the relative increase in performance and potential loss in accuracy with two chemistry acceleration techniques namely Clustering, Dynamic Mechanism Reduction (DMR) and their combination. The results show that the different chemistry acceleration techniques do not compromise the time averaged flow statistics. However, there are some differences in NO and CO emissions. Chemistry acceleration techniques yield up to ∼3 times speed-up of the simulation.
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Krzhizhanovskaya, V. V., M. A. Zatevakhin, A. A. Ignatiev, Yu E. Gorbachev, W. J. Goedheer, and P. M. A. Sloot. "A 3D Virtual Reactor for Simulation of Silicon-Based Film Production." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-3120.
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In this paper we introduce a Grid-based Virtual Reactor, a problem-solving environment that supports detailed numerical study of industrial thin film production in Plasma Enhanced Chemical Vapor Deposition (PECVD) reactors. We describe the physics and chemistry underpinning the deposition process, the numerical approach to simulate these processes on advanced computer architectures as well as the associated software environment supporting computational experiments. In the developed 3D model we took into account all relevant chemical kinetics, plasma physics and transport processes that occur in PECVD reactors. We built an efficient problem-solving environment for scientists studying PECVD processes and end-users working in chemical industry and validated the resulting Virtual Reactor against real experiments.
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Moiseeva, Elena F., Victor L. Malyshev, Dmitriy F. Marin, Nail A. Gumerov, and Iskander Sh Akhatov. "Molecular Dynamics Simulations of Nanobubbles Formation Near the Substrate in a Liquid With Dissolved Gas." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37050.
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Nanobubbles appearing on the interface between liquid and the hydrophobic substrate play an important role in various natural and industrial processes. The current study presents the MD simulations of surface nanobubbles on the liquid-solid interface, where the liquid phase consists of argon and dissolved neon, while the gaseous phase consists of neon and argon vapor. The interactions between all the particles are determined by the Lennard-Jones potential. The contact angle is studied as a function of the Lennard-Jones parameters for the liquid-solid and gas-solid interactions. Moreover, the influence of gas concentration on the system behavior is studied. The simulations are performed for the systems of tens nm in size, which contain up to 8 million molecules. The computations are accelerated using modern computational methods and algorithms as well as using high-performance hardware such as graphic processors.
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Cristea, Eugen-Dan, and Pierangelo Conti. "Numerical Investigation on Multiphase Reacting/Combusting Turbulent Flows: Aerodynamics, Kinetics, Heat and Mass Transfer Inside a Cement Kiln Precalciner." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-5033.
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Abstract This paper reports the modeling work to develop a computational fluid dynamics (CFD) engineering application, based on an appropriate 3D mathematical model able to perform the thermo-fluid dynamic numerical simulation of multiphase reacting/ combusting turbulent flows within a precalciner of an industrial four-stage cyclone preheater/precalciner cement kiln. In the precalciner furnace the hot micron-sized limestone (calcite/dolomite) meal, held in suspension, is quiet completely converted to quicklime (CaO(s)), and the CO2(g) by-product is driven-off during calcination process. Since, the thermal decomposition mechanism is a very endothermic reaction, the necessary heat is balanced by pulverized petcoke combustion. These major physical and chemical processes inside the precalciner are properly described by the 3-D Favre-averaged Navier-Stokes equations with the species transport equations, the energy equation and the state equation, to be solved by an Eulerian-Lagrange approach. The CFD solver employed in this study is the commercial CFD code ANSYS Fluent R18.2. The used built-in models/sub-models include turbulence models and near-wall treatment, model of traditional air-pulverized petcoke combustion, pulverized-limestone calcination model, as well as the sub-models for radiation heat transfer and turbulence-chemistry interaction. They are used to formulate the closures of the unclosed terms in the PDEs system. In summary, the trends of predicted results of limestone calcination and petcoke/TDF combustion processes in an industrial precalciner furnace are reasonable fair in confront to operation data measurable in the harsh environment conditions, typical for the pyroprocessing systems. The developed CFD engineering application can be used as an effective design tool for preliminary examination of the global effect of thermal-flow aerodynamics and turbulence on the precalciner processes.
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Savarese, Matteo, Alberto Cuoci, Ward De Paepe, and Alessandro Parente. "Automatic extraction of Chemical Reactor Networks from CFD data via advanced clustering algorithms." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-349.
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The design of cleaner and more sustainable combustion technologies represents nowadays a key task. Reliable numerical models, able to cope with a large variety of configurations, combustion processes and fueling mixtures are needed, especially for future applications in combustion monitoring and control, for thermal and environmental performances, which are of critical importance. In this work, alternative, low-computational cost modelling tools for pollutants and thermal efficiency predictions, represented by Chemical Reactor Networks (CRN), are designed from Computational Fluid Dynamic (CFD) simulations assessing a novel methodology, by exploring new possibilities offered by Machine Learning (ML) algorithms. In particular, unsupervised learning approaches are employed, in order to extract the key features of the system flow-field, adopting advanced clustering algorithms, such as Local Principal Component Analysis (LPCA) and K-Means, thus providing an efficient and automatic identification of similar thermo-chemical state compartments in the computational domain. The identified zones are modelled in a post-processing phase as a network of interconnected chemical reactors, and detailed kinetic mechanisms are employed for low concentration pollutants predictions. The case study, a quasi-industrial, flameless-capable combustion furnace, fed with methane-hydrogen mixtures in different compositions at a nominal power of 15 kW, has been investigated numerically by performing 2D CFD simulations with reduced chemistry and subsequently CRN simulations has been carried out with detailed kinetics, adopting the aforementioned approach. Results are validated upon experimental data, in order to provide a novel methodology for CRN design applications, which can be suited for future GTs applications.