Continuous Stirred Tank Reactor Research Articles

Model-predictive fault-tolerant control of safety-critical processes based on dynamic safe set

Industrial systems and chemical plants heavily rely on automation and control systems for seamless operations. However, the susceptibility of these systems to various faults poses threats to processes, leading to economic losses and safety risks. Here, a robust fault-tolerant control (FTC) strategy is developed that can take proactive measures during faults involving in-time activation of a backup controller, to ensure that the system remains within safe operational limits. It is based on the Dynamic Safe Set (DSS) which is the set of initial process states that meet safety constraints at all times, and the dynamic safety margin (DSM) which is the minimum distance from the DSS boundary. For just-in-time corrective action, a critical fault function is introduced, defined as the time required by the system to cross the DSS boundary under the nominal controller only. This critical fault function is calculated offline and is integrated with a real-time fault size estimation to formulate the controller reconfiguration logic to keep system within DSS. A linear functional observer is used to estimate fault size, combined with a predictive scheme, to enhance robustness during the transient period of fault estimation. This configuration avoids unnecessary control actions while ensuring timely intervention. The proposed FTC strategy is tested on an exothermic Continuous Stirred Tank Reactor (CSTR) case study. The results demonstrate the strategy's effectiveness in handling process faults, ensuring both stability and safety constraints are met. Thus, this paper contributes to the advancement of FTC ensuring the resilience of industrial systems in the face of unforeseen challenges.

Wastewater biotreatment and bioaugmentation for remediation of contaminated sites at an oil recycling plant

ABSTRACT This work focused on the biotreatment of wastewater and contaminated soil in a used oil recycling plant located in Bizerte. A continuous stirred tank reactor (CSTR) and a trickling filter (TF) were used to treat stripped and collected wastewater, respectively. The CSTR was started up and stabilized for 90 days. Over the following 170 days, the operational organic loading rates of the TF and the CSTR were around 1,200 and 3,000 mg chemical oxygen demand (COD) L−1 day−1, respectively. The treatment efficiency was 94% for total petroleum hydrocarbons, 89.5% for COD, 83.34% for biological oxygen demand (BOD5), and 91.25% for phenol. Treated industrial wastewater from the TF was used for bioaugmentation (BA) of contaminated soil. The assessment of the soil took 24 weeks to complete. The effectiveness of the soil BA strategy was confirmed by monitoring phenolic compounds, aliphatic and polycyclic aromatic hydrocarbons, heavy metals, and germination index. The biodegradation rate of contaminants was improved and the time required for their removal was reduced. The soil bacterial communities were dominated by species of the genera Mycobacterium, Proteiniphilum, Nocardioides, Luteimicrobium, and Azospirillum, which were identified as hydrocarbon and phenol-degrading bacteria.

Fault Detection Strategy of Partial Least Squares Based on Temporal Neighborhood Difference

ABSTRACTAiming at the difficulty of detecting time‐lag faults in dynamic processes, a fault detection strategy based on time neighborhood difference (TND) is proposed, and it is introduced into the partial least squares (PLS) method to suggest the PLS‐TND fault detection method. The TND method takes the mean to the multibatch training set to obtain a baseline training set, and it constructs the mean squared Euclidean distance (MSED) statistic by calculating the average distance between the sample's first k‐moments neighborhood samples and samples at the same moment in the baseline training set. The TND method can help the PLS method to effectively detect time‐lag faults and significantly improve the fault detection capability of PLS by measuring the overall positional difference between the temporal neighborhood sample set of the sample and its temporal neighborhood sample set in the baseline training set. The PLS‐TND method is compared with some classical fault detection methods through a numerical simulation process and a Continuous Stirred Tank Reactor (CSTR) system design fault detection experiment. The PLS‐TND method gives the best performance of fault detection.

Adaptive Robust Control of Nonlinear Constrained Stirred‐Tank Reactors via Self‐Organizing Fuzzy Neural Network

ABSTRACTThis paper presents an adaptive robust constraint controller for a continuous stirred tank reactor (CSTR) system based on a self‐organizing fuzzy neural network (SOFNN). Due to the high complexity of the chemical reactions, the CSTR system contains many strong nonlinearities and uncertainties. This is the first time to introduce the SOFNN into the adaptive controller design of the CSTR system, which improves the adaptability to dynamic system changes through the adjustment of the fuzzy network structure. Meanwhile, the time‐varying integral barrier Lyapunov functions (TVIBLFs) are employed to ensure the dimensionless reactant concentration and mixture temperature within a reasonable scope, which can improve the stability and safety of the CSTR system. Based on Lyapunov stability analysis, all the signals in a closed‐loop system are ultimately bounded. Simulation results substantiate the efficacy of the proposed control scheme.

The kinetics of a two-stage completely stirred tank reactor for treating tofu wastewater by anaerobic digestion

ABSTRACT Tofu wastewater is an inevitable by-product of the bean curd production process. In this study, we used a two-stage continuous stirred tank reactor (CSTR) to treat tofu wastewater. The kinetic analysis of two CSTR reactors was conducted utilizing the material accounting model, microbial accounting model, and first-order kinetic model. The results demonstrated that the methane production coefficient of reactor 1 was 0.00573 LCH4·mg−1 greater than that of reactor 2, which is 0.00533 L CH4·mg−1. The first-order kinetic constants of reactor 1 were poorer than those of reactor 2, and their values are 0.15700 d−1 and 0.11574 d−1, respectively. The microbial yield coefficients of reactors 1 and 2 were 0.009648 and 0.060704, respectively. Under different organic loading rates, the average microbial concentrations were 25.24000 and 2.706667 mg·L−1 for reactors 1 and 2, respectively. The operational performance of the two-stage CSTR anaerobic digestion system was evaluated in order to design a novel and unique method as well as provide a new perspective for enhancing the performance of anaerobic digestion.

Continuous Flow Optimisation of the Pudovik Reaction and Phospha-Brook Rearrangement Using DBN

Flow chemistry has shown significant versatility over the last two decades, offering advantages in efficiency, scalability, and sustainability. In this study, the continuous stirred tank reactor (CSTR) was used to optimise the synthesis of α-hydroxyphosphonates via the Pudovik reaction and their subsequent conversion to phosphates through the phospha-Brook rearrangement. The study highlights that using CSTRs allows for better control over reaction parameters, leading to reduced reaction times and improved yields compared to traditional batch methods. The optimised conditions successfully facilitated a range of organophosphates, including electron-rich and electron-poor derivatives, with high efficiency. Additionally, a one-pot tandem process combining the Pudovik reaction and the phospha-Brook rearrangement was developed, reducing reaction times to two hours while maintaining comparable yields. This work demonstrates the potential of CSTRs in flow chemistry for synthesising complex organophosphorus compounds, achieving higher reaction yields and shorter reaction times, highlighting the effectiveness of continuous flow methodologies.

Valorization of paper-mill sludge laden with 2-chlorotoluene using hydroxyapatite@biochar nanocomposite to enrich methanogenic community: A techno-economic approach

While several studies have investigated the anaerobic digestion of paper-mill sludge (PMS), this technology suffers from nutrient insufficiency, inhibition by aromatic compounds, and low bio-CH4 yield. Hence, PMS was anaerobically co-digested with chicken manure (CM) and supplemented by hydroxyapatite@biochar (HAP@BC) nanocomposite for enhancing 2-chlorotoluene degradation and enriching the methanogenic archaea. Multiple continuous stirred tank reactors (CSTRs) were operated at 12.6 h hydraulic retention time (HRT), using PMS (R1), CM (R2), PMS + CM (R3), PMS + CM+100 mg HAP/L (R4), and PMS + CM+100 mg HAP@BC/L (R5). The maximum bio-CH4 yield of 147.5 ± 9.1 mL/g COD and 2-chlorotoluene removal of 91.2 ± 6.8 % were obtained from R5, experiencing a sufficient C/N ratio of 14.7 and the highest activities of acidogenesis (42.0 %), acetogenesis (37.9 %), and methanogenesis (42.1 %). The abundances of Euryarchaeota, Bacteroidota, and Chloroflexi at the phylum level, and Pseudomonas, and Bacillus at the genus level could highly contribute to the dechlorination mechanism and acetate transformation into CH4. This biomass-to-bioenergy project (10 m3/d capacity) could benefit from pollution reduction, biogas recovery, and carbon credit, giving 5.6 yr payback-period, 3503 USD net present value, and 12.1 % internal rate of return. Because R5 exhibited an efficient techno-economic anaerobic biodegradation performance, future studies are required to optimize its HRT condition and HAP@BC dosage.

Bacterial synergy and relay for thermophilic hydrogen production through dark fermentation using food waste

BackgroundFood waste is a significant global issue, with 1.3 billion tons generated annually, a figure expected to rise to 2.1 billion tons by 2030. Conventional disposal methods, such as landfilling and incineration, present environmental challenges, including methane emissions and pollution. Hydrogen production through dark fermentation presents a sustainable alternative, offering both waste management and renewable energy generation. This study investigates the bacterial synergy and relay mechanisms involved in thermophilic H2 production using food waste as a substrate. PurposeThe primary aim of this research was to analyze the metabolic pathways and dynamics of functional genes prediction during thermophilic H2 production from food waste, focusing on the role of bacterial consortia in enhancing H2 yields. MethodsA continuous stirred-tank reactor (CSTR) was operated using food waste as the substrate and a thermophilic bacterial consortium as the inoculum. The study utilized genomic analysis to monitor changes in bacteriobiome composition over time and to correlate these changes with H2 production. Volatile fatty acids (VFAs) and H2 production rates were analyzed using gas chromatography and high-performance liquid chromatography (HPLC). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was employed to identify functional genes involved in the fermentation process. ResultsThe study identified key bacterial species, including Caproiciproducens and Caproicibacter, that dominated during the later stages of H2 production, replacing earlier dominant species such as Clostridium. These shifts in bacterial dominance were strongly correlated with sustained H2 production rates ranging from 353 to 403 mL·L−1·h−1, with H2 concentrations between 55 % and 62 % (v/v). Functional gene analysis revealed significant pathways related to polysaccharide degradation, glycolysis, and dark fermentation. ConclusionsThis study highlights the importance of bacterial synergy and relay in maintaining continuous H2 production from food waste under thermophilic conditions. The findings provide insights into optimizing biohydrogen production processes, emphasizing the role of specific bacterial species in enhancing efficiency. These results contribute to the development of sustainable waste management strategies and renewable energy production.

Innovative system to maximize methane production from fruit and vegetable waste.

Anaerobic digestion of fruit and vegetable waste (FVW) offers an environmentally friendly alternative for waste disposal, converting it into methane for energy recovery. Typically, FVW digestion is conducted in a continuously stirred tank reactor (CSTR) due to its ease of use and stability with solid concentrations between 5 and 10%. However, CSTRs are limited to organic loading rates (OLRs) of about 3kg COD/m3.day, resulting in large reactor volumes, low methane productivity, and costly wet digestate handling. This work introduces a novel method for methane production from FVW using a high-rate reactor system. The proposed approach involves grinding, centrifuging, and/or pressing the FVW to separate it into liquid and solid phases. The liquid phase is then digested in an up-flow anaerobic sludge blanket (UASB) reactor, while the solid phase undergoes digestion in a dry methanization reactor. A model incorporating all biological reactors was implemented in the Anaerobic Digestion Model 1 (ADM1) to provide a theoretical basis for the experimental development of this system. The current simulation scenarios offer initial references for operating the experimental system, which will, in turn, generate data for further model refinement. For instance, constrained liquid-gas mass transfer was considered for dry fermentation, with additional potential biochemical kinetic limitations to be incorporated following on experimental evidence. The success of this system could enable energy recovery in 72 Central Wholesale Markets across Brazil, offering a critical tool for planning, operating, and optimizing such systems.

Q‐learning‐based H∞ control for LPV systems

AbstractIn this paper, a model‐free H∞ control method is developed for discrete‐time linear parameter‐varying (LPV) systems by leveraging Q‐learning, which employs the data encompassing system states, control inputs, exogenous disturbance, and time‐varying convex hull rather than the dynamical model known a priori. A policy iteration algorithm presented via linear matrix inequality formed by such collected data is developed with convergence guarantee. Our analysis demonstrates that, in the presence of sufficiently rich disturbances, the attenuation level converges to a lower value than the traditional H∞ control solution derived from an exact LPV model. A numerical example is demonstrated to verify the stabilization, disturbance attenuation, adaptivity, and convergence of the H∞ attenuation level. Moreover, a continuous stirred tank reactor (CSTR) approximated by a discrete‐time LPV system is employed to show the practical potential of the developed model‐free approach.

A dynamic system reliability analysis model on safety instrumented systems

This paper introduces a novel hybrid dynamic model for complex systems reliability assessment. The model synergizes expert knowledge elicitation and an enhanced Dempster-Shafer Theory (DST) with Dynamic Bayesian Networks (DBNs) modeling, aiming to surmount the limitations such as uncertainty and static modeling inherent in traditional methods. The proposed model is deployed on a Safety Instrumented System (SIS) designed to prevent runaway reactions within a Continuously Stirred Tank Reactor (CSTR), considering factors such as system degradation, human interventions, and proof testing on system reliability. The analysis pinpointed the logic solver subsystem as the principal vulnerability within the assessed SIS, leading to targeted recommendations to bolster system reliability. The outcomes offer insights for a wide range of safety-critical systems aiming to augment the safety and efficacy of SISs, thereby advancing safety and resilience management across various complex engineering systems, particularly in contexts where field data is scant.

Can ZnO/Cu catalyst provide promising activity for glycerol direct dehydrogenation? A combined density functional theory and coverage-dependent microkinetics study

Non-oxidative dehydrogenation (NODH) reaction of glycerol is a perfect atom economical technical route to produce higher-value 1,3-dihydroxyacetone (DHA). Cu-based catalyst (especially ZnO/Cu system), is known as active species for alcohol dehydrogenation, which may be also promising one for glycerol NODH. In this study, we combine coverage-dependent free energy profile using first-principle calculation, and microkinetic model, to investigate the NODH of glycerol on the ZnO/Cu(111) surface, and a detailed comparison is made with Cu (111) surface. The coverage-dependent microkinetic model takes into account the lateral adsorbate–adsorbate self-interactions and cross-interactions, and their effect on binding energy of both intermediate and transition state. Besides, it guarantees the reaction kinetics is based on the coverage self-consistent between surface model and microkinetic result under practically reaction conditions. Compared with coverage-dependent kinetics simulation (20 %–30 %), coverage-independent model overestimates the DHA selectivity on Cu (111) (over 90 %). Our coverage-dependent kinetics simulation illustrates that both glycerol conversion and DHA selectivity are most determined by the first dehydrogenation step (OH bond scission) of glycerol on Cu (111). However, when ZnO cluster adsorbed on the Cu (111) surface, ZnO cluster promotes the electron transferring between glycerol and Cu sites, which accelerates OH bond scission process of glycerol. Under coverage-dependent microkinetic model, the turnover frequency and selectivity of DHA on ZnO/Cu(111) get much improvement compared with Cu (111). Finally, the superior of ZnO/Cu(111) is further proved by continuous stirred tank reactor simulation, where NODH of glycerol need shorter residence times or lower temperature to reach 100 % conversion, as well as keep higher DHA selectivity.

Valorization of process water from hydrothermal carbonization of food waste by dark fermentation

Hydrothermal carbonization (HTC) of food waste produces hydrochar—a suitable biofuel—and process water (PW), which has a high organic content suitable for material and energy recovery. In this work, we study the effect of pH (4.8, 5.3, and 6.0) and organic loading rate (OLR; 2.5, 5.0, and 7.5 gCOD L−1 d−1) throughout the dark fermentation (DF) of PW from the HTC of food waste (180 °C, 1 h) in a continuous stirred tank reactor. The highest hydrogen yield (197.5 mL H2 L−1 d−1) was reached at pH 4.8 and OLR 5 gCOD L−1 d−1, associated with a prevalence of Clostridium bacteria. The highest volatile fatty acids concentration (10.2 gCOD·L−1) was achieved at pH 4.8 and OLR 7.5 gCOD L−1 d−1, with a dominance of Actinobacteria phylum. The integrated system HTC-DF allowed a potential energy recovery of 11.2 MJ kg−1.

MIMO system identification and uncertainty calibration with a limited amount of data using transfer learning

Multiple-input multiple-output (MIMO) systems are fundamental in numerous advanced engineering applications, from aerospace to telecommunications, where precise system identification is critical for optimal performance. However, the identification of such systems often faces significant hurdles due to data scarcity, with existing approaches typically requiring substantial amounts of data for effective training. Addressing this challenge, this paper introduces a novel transfer learning framework designed specifically for MIMO system identification under conditions of limited data and inherent uncertainties. The proposed framework is applied to two case studies: the first in metal additive manufacturing, specifically the laser-blown powder-directed energy deposition as the source domain and the laser hot wire-directed energy deposition as the target domain, and the second involving a nonlinear case study of a continuous stirred-tank reactor (CSTR) with a temperature-dependent reaction. The results underscore the framework's effectiveness in capturing the dynamics of the target systems, including the ability to effectively model nonlinear dynamics. Comparative analyses highlight the benefits of employing dimensionless numbers in dynamic system modelling, offering reduced dimensionality, more physical meaning, and increased model accuracy. Overall, the proposed framework presents a promising approach to enhance system identification in MIMO systems with limited data and uncertainties, with potential applications across diverse domains.

Nonideal mixing effect based on Cholette’s model on Van de Vusse reaction selectivity dynamics in an isothermal continuous stirred-tank reactor

Abstract The nonideal mixing effect on the Van de Vusse reaction dynamic in a continuous stirred-tank reactor (CSTR) has rarely been studied in literature. This work presents the mixing operation condition and kinetic reaction constant effects on the Van de Vusse reaction selectivity dynamic in an isothermal CSTR based on Cholette’s model. The mixing condition effect is defined using the parameter μ = n/m, where n is the flow fraction to the reactor and m is the dead space fraction in the reactor. In Cholette’s model, μ = 1 and μ ≠ 1 represent, respectively, the ideal and nonideal mixing operations. Herein, the final desired product yield steady state in the phase plane is first constructed under reaction constant and mixing condition combinations, indicating that the larger the μ values, the higher the desired product steady-state yield. In the selectivity dynamic simulations, the phase plane is divided into transient extreme yield region and region of monotonic variation in yield. It is found for the first time that a higher μ value leads to a decrease in the transient maximum selectivity region area and an increase in the transient minimum selectivity region area in the phase plane. Moreover, the trajectory from the start up to the final steady state will be changed due to nonideal mixing. These results have not been published and have direct applications in real reactor design and operation. Since a CSTR with ideal mixing is hardly attainable in practice, our results benefit the real reactor design and startup policy development in the desired product transient maximum yield.

Application of continuous stirring tank reactor for controllable synthesis of Cu7S4 nanocrystals

As a typical model for a chemical reactor in industry, the continuous stirring tank reactor has been widely used in waste-water treatment, industrial catalysis, and biological fermentation as well as great application prospects in the synthesis of nanomaterials. In this report, a convenient lab-scale continuous stirring tank reactor was assembled and utilized to synthesize Cu7S4 nanocrystals by injecting cupric bromide and sulfur solution in the presence of ascorbic acid and polyethyleneimine at 60 °C. The morphology control of Cu7S4 nanocrystals was accomplished by simply adjusting the agitation speed. Cu7S4 nanofibers with an average diameter of 48 ± 4.42 nm and a length of several micrometers were obtained at 80 rpm. Cu7S4 nanoplates with a thickness of 89 ± 13.56 nm and a plane size of 103 ± 16.47 nm were synthesized at 90 rpm, and the size of the nanoplates was regulated by continuously increasing the agitation speed from 90 rpm to 1000 rpm. Furthermore, the influences of two additional conditions (the mean residence time and the concentration of the feed solution) on the morphology of Cu7S4 nanocrystals, were also investigated. Therefore, these results could facilitate the understanding of the behaviors of CSTR in the synthesis of Cu7S4 nanocrystals and could also provide an important reference for the continuous synthesis of other nanoparticles.

Advanced Control Strategies for Continuous Stirred Tank Reactors:

This paper investigates the control of a Continuous Stirred Tank Reactor (CSTR), with a primary emphasis on achieving temperature stability and optimizing reactant conversion. The CSTR poses challenges due to its nonlinear and exothermic behavior. To address these challenges, Model Predictive Control (MPC) is employed, a powerful strategy for handling complex systems. Additionally, Particle Swarm Optimization (PSO) is introduced to fine-tune MPC parameters, ensuring optimal performance. By integrating PSO with MPC, this study enhances control capabilities, specifically targeting the intricate demands of CSTR systems.

Fast pyrolysis of paper sludge in a continuous stirred-tank reactor and liquid-liquid extraction of benzenoid aromatics from fast pyrolysis bio-liquid

Paper sludge is a solid waste in paper mills and is conventionally treated by, e.g., landfill, composting, and incineration. Paper sludge contains paper production fillers and lignocellulosic biomass, both of which can be recycled to recover circular minerals and produce bio-based fuels and chemicals by, e.g., thermochemical recycling technology namely fast pyrolysis, for circular bioeconomy. In this paper, three different paper sludge samples collected in The Netherlands and Spain were analyzed by thermogravimetric analysis, moisture analysis, ash analysis, CHNS elemental analysis, powder X-ray diffraction, and X-ray fluorescence spectrometry. Fast pyrolysis of paper sludge was carried out in a lab-scale continuous stirred tank reactor at 500 ± 10 °C with a paper sludge feeding rate of 1 kg h−1. The recovery of circular minerals, which are mainly calcium carbonate, is 87.1 ± 2.2 % (on mineral basis). The yields of fast pyrolysis bio-liquid and biochar are 49.2 ± 6.7 wt% (on biomass basis, equivalent to 23.7 ± 2.2 wt% on paper sludge basis) and 23.8 ± 8.3 wt% (on biomass basis). Fast pyrolysis bio-liquid is a diluted aqueous containing various oxygenates (major, including alcohols, acids, benzenoid aromatics, aldehydes, ketones, ethers, and esters) and hydrocarbons. Liquid-liquid extraction of the fast pyrolysis bio-liquid using CH3OH/H2O and SO2/H2O was further performed to obtain an improved bio-liquid with relatively high concentration of the desired bio-based chemicals (namely benzenoid aromatics with the concentration of 53.1–65.9 area%). Both SO2/H2O and CH3OH/H2O show high liquid-liquid extraction efficiency to concentrate the benzenoid aromatics for 3.4–11.0 times. This work shows the fast pyrolysis followed by liquid-liquid extraction for the valorization of paper sludge, of which the former has been recently demonstrated on a pilot-scale unit in industry. However, the latter still needs to be further developed by, e.g., focusing on the extraction solvent and continuous liquid-liquid extraction process integrated to fast pyrolysis.

System identification of a nonlinear continuously stirred tank reactor using fractional neural network

Chemical processes are vital in various industries but are often complex and nonlinear, making accurate modeling essential. Traditional linear approaches struggle with dynamic behaviour and changing conditions. This paper explores the advantages of the new theory of fractional neural networks (FNNs), focusing on applying fractional activation functions for continuous stirred tank reactor (CSTR) modeling. The proposed approach offers promising solutions for real-time modeling of a CSTR. Various numerical analyses demonstrate the robustness of FNNs in handling data reduction, achieving better generalization, and sensitivity to noise, which is crucial for real-world applications. The identification process is more generalized and can enhance adaptability and improve industrial plant management efficiency. This research contributes to the growing field of real-time modeling, highlighting its potential to address the complexities in chemical processes.

Rate expression model from thermodynamics application and optimal kinetic parameters determination for urea synthesis and production process

Urea, an essential organic fertilizer, enhances soil fertility by providing 0.466 nitrogen for maximum crop yield. In this study, urea is synthesized from NH3 and CO2 in an equilibrium reaction process adhering to Le Chatelier's principle, maintained under process conditions: flow rate of 63.5 kg/s, temperature of 184 °C, and pressure of 160 kg/cm2. A new rate expression model, formulated in terms of extent of reaction and mole fraction, was developed based on mass action relations and thermodynamic models. Two industrial reactors were considered: a plug flow reactor (PFR) at Notore and a continuous stirred tank reactor (CSTR) at Indorama plants. Transient reactor models, based on material and energy balance conservation principles, were numerically resolved using MATLAB version 2020 with specified input conditions. A non-linear regression statistical optimization model was employed to refine kinetic parameter values, ensuring optimal and high-quality urea yield. Model validations were conducted using literature data, revealing higher urea yields of 0.726 and 0.7032 for the CSTR and PFR, respectively. Deviations (0.134, 0.10 to 1.135 and 0.635, 0.326 to 0.850) and root mean square errors (RMSE) (0.043, 0.033 to 0.193 and 0.137, 0.087 to 0.162) were observed when validated against plant and literature values for the CSTR and PFR respectively. The refined kinetic parameters (activation energies, Arrhenius constants, and rate constants) exhibited negligible deviations (0.0004–0.0466 and 0.0004 to 0.0491) and RMSE (0.0228, 0.0055, and 0.0256 and 0.0241, 0.0096, and 0.0269) when validated against plant data, significantly enhancing urea yield in CSTR and PFR reactors respectively.