Stirred Tank Reactor Research Articles

A fascinating exploration into nitrite accumulation into low concentration reactors using cutting-edge machine learning techniques

In the last couple decades, more use of nitrogenous chemical fertilizers and improper disposable of wastewater has harmed water and it cause water pollution. Low concentrated Nitrite (NO2) is the one of hazardous pollution and it is difficult to remove through biological processes, while it occurs in low concentration. Many technologies have been developed to accumulate NO2 in the mainstream. However, most of them use chemical inhibitors for nitrite oxidizing bacteria (NOB). In past studies high concentrated reactor performance have been modeled using mathematical models. In this study, machine learning application (MLA) was applied to model the performance of reactors. The reactor was low concentrated, continuous stirred tank reactor (CSTR) with in-fluent total ammonia nitrogen (TAN) concentration was (∼30PPM-TAN and ∼50PPM-TAN) and lower output TAN concentration was (∼1PPM-TAN). However, 216 days of water treatment data from CSTR were used, and the CSTR’s efficiency (%) of nitrite accumulation was estimated using in-fluent and effluent quantities. Then efficiency is predicted with 70 % of the data that is used to train the algorithms. Confusion matrix was used to access the performance of algorithms and actual and predicted classes (efficiencies) were compared. The DTC and XGB over-performed other algorithms.

Heat Transfer Performance Studies of Packed Bed Distillation Setup

Heat transfer performance studies done in a setup involving a stirred tank reactor and packed bed distillation column. Stirred tank reactor consists of double pitch blade impeller with rotational speeds varied from 40-120 rpm. The water-air system was taken into consideration. The reactor is jacketed with Therminol 55 as a heating medium. Therminol 55 is used as a heating medium because of its higher specific heat and boiling point. Column comprises of 3 packed bed segments, each of 1m in height and jacketed with Therminol 55. Therminol 55 is used as a source of indirect heating to negate any heat losses in the column. The column is randomly packed with Mellapak250Y packing. The data collected by experiments especially characterizes the packed bed distillation setup and heat transfer performance. The work evaluates effect of agitation on overall heat transfer coefficient and mass flow/ boil up rate calculated theoretically and experimentally. Use of efficient correlations along with various necessary dimensionless numbers and rudimentary parameters were clubbed to form a MATLAB model useful for verification of theoretical data. The comparison of theoretical and experimental overall heat transfer coefficient and mass flow/boil up rate is done. The study done on the setup used shows conclusions regarding overall heat transfer coefficient and mass flow/boil up rate with respect to agitator speed and Reynolds number. The theoretical calculated values of overall heat transfer coefficient and mass flow/boil up rate are compared with the experimental ones and conclusions taken out regarding the reactor and column contribution. Finally, the Reynolds number and mass flow/boil up rate relation is shown

Continuously stirred tank reactor for oil-suspended thermochemical energy storage systems for CuSO4·5H2O

Thermochemical energy storage can be used for heating applications, thereby helping to cut down on greenhouse gases from burning non-renewable fuels by offering a solution for seasonal heat storage. In the scope of the EU project RESTORE, a thermochemical energy storage is used to store low temperature waste heat and use it for district heating. This study presents the thermochemical energy storage as a new continuously stirred tank three-phase suspension reactor for storing heat from renewable sources or waste heat with almost no losses, in reaction. Therefore, the reactor was built of glass, where process parameters, such as the mean residence time, have been varied to obtain optimized results for thermochemical energy storage operating at the fixed temperature of 125 °C chosen due to project restrictions. The heat is used to dehydrate CuSO4·5H2O in the charging step and recovered by rehydrating CuSO4·3H2O or CuSO4·H2O, respectively, while generating heat in the discharging step. Using a mixture of silicone and mineral oil to prevent foaming, conversion rates of over 94 % to CuSO4·H2O were reached in the charging step with the continuously stirred tank reactor, with 1 kW thermal power, 150 mm diameter and 900 mm height in continuous operation. In discharging operation, 100 % conversion from CuSO4·H2O to CuSO4·5H2O was measured. This paper lays the groundwork for a new technology for thermochemical heat storage in low temperature application.

Quantitative Online Monitoring of an Immobilized Enzymatic Network by Ion Mobility-Mass Spectrometry.

The forward design of in vitro enzymatic reaction networks (ERNs) requires a detailed analysis of network kinetics and potentially hidden interactions between the substrates and enzymes. Although flow chemistry allows for a systematic exploration of how the networks adapt to continuously changing conditions, the analysis of the reaction products is often a bottleneck. Here, we report on the interface between a continuous stirred-tank reactor, in which an immobilized enzymatic network made of 12 enzymes is compartmentalized, and an ion mobility-mass spectrometer. Feeding uniformly 13C-labeled inputs to the enzymatic network generates all isotopically labeled reaction intermediates and products, which are individually detected by ion mobility-mass spectrometry (IMS-MS) based on their mass-to-charge ratios and inverse ion mobilities. The metabolic flux can be continuously and quantitatively monitored by diluting the ERN output with nonlabeled standards of known concentrations. The real-time quantitative data obtained by IMS-MS are then harnessed to train a model of network kinetics, which proves sufficiently predictive to control the ERN output after a single optimally designed experiment. The high resolution of the time-course data provided by this approach is an important stepping stone to design and control sizable and intricate ERNs.

Enhanced wastewater treatment with an AnF-AAO system for improved internal carbon source utilization

The main challenge in removing nutrients from municipal wastewater in China is the lack of available carbon sources. While hydrolysis acidification tanks can improve wastewater biodegradability by effectively utilizing internal carbon sources, high sludge concentrations are difficult to control in traditional tank variants. In this study, an innovative anaerobic filter (AnF) hydrolysis acidification reactor composed of a continuously stirred tank reactor (CSTR) and cloth media filter was designed to regulate and maintain high sludge concentrations in the hydrolysis acidifier. The reactor was used as a pretreatment unit for the anaerobic/anoxic/oxic (AAO) units and combined into an AnF-AAO system to explore the effectiveness of internal carbon source utilization in wastewater. The results indicate that as the sludge concentration in the hydrolysis acidifier increased, the hydrolysis and acidification processes became more efficient. The optimal sludge concentration was 40 g/L, which significantly increased the production of soluble chemical oxygen demand and volatile fatty acids. Above this concentration, the efficiency decreased. Compared to traditional AAO processes, the AnF-AAO system achieved superior total nitrogen and phosphorus removal with shorter hydraulic retention times and reduced sludge production by a significant amount of 35%. Due to its capacity for enhancing internal carbon source utilization, the AnF-AAO system constitutes a promising approach for sustainable urban wastewater treatment.

A dynamic compartmental model of a sequencing batch reactor (SBR) for biological phosphorus removal

ABSTRACT Bioreactors are usually modelled as continuous stirred tank reactors (CSTRs) or CSTRs connected in series (Tanks-In-Series configuration). In large systems with non-ideal mixing, such approaches do not sufficiently capture the complex hydrodynamics, leading to model inaccuracies due to the lumping of spatial gradients. Highly detailed computational fluid dynamics (CFD) models provide insight into complex hydrodynamics but are computationally too expensive for flow-sheet models and digital twin applications. A compartmental model (CM) can be a middle-ground by providing a more realistic representation of the hydrodynamics and still being computationally affordable. However, the hydrodynamics of a plant can be very different under varying flow conditions. Dynamic CMs can capture these changes in an elegant way. So far, the application of CMs has been limited mostly to continuous flow systems. In this study, a dynamic CM of a sequencing batch reactor (SBR) is developed for a bio-P removal process. The SBR comes with challenges for CM development due to its distinct operational stages. The dynamic CM shows significant improvements over the CSTR model (using the same biokinetic parameters) for dissolved oxygen and phosphate predictions reducing the need for model recalibration that can lead to over-fitting and limited extrapolation capability of the model.

Shrinking economic MPC of constrained nonlinear systems: An event‐triggered constraint relaxation approach

AbstractThis article proposes a novel event‐triggered economic model predictive control (EMPC) scheme with shrinking prediction horizon of nonlinear systems subject to the constraints on the state and control. Some auxiliary positive‐definite functions at economic optimal equilibrium points are defined for economic performance functions. The optimal value function of the auxiliary function is used to design an adjustable stability constraint, which is imposed on the original EMPC optimization problem. The stability constraint is relaxed to design the triggered scheme avoiding Zeno behavior, while the prediction horizon is shrinked in different degrees by judging whether the last state within the triggered interval is in the terminal region. The involved parameter can be used to achieve a sensible compromise between system performance and computational complexity. The sufficient conditions guaranteeing the recursive feasibility and stability of the EMPC are derived. Finally, the effectiveness of the algorithm is illustrated by a continuously stirred tank reactor example.

Data‐driven nonlinear state observation for controlled systems: A kernel method and its analysis

AbstractThis work proposes a data‐driven state observation algorithm for nonlinear dynamical systems, when the true state trajectory is not measurable and hence the states information needs to be reconstructed from input and output measurements. Such a reduction is formed by kernel canonical correlation analysis (KCCA), which (i) implicitly maps the available input–output data into a higher‐dimensional feature space, namely the reproducing kernel Hilbert space (RKHS); (ii) finds a projection of the past input–output data and a projection of the future input–output data with maximal correlation; and (iii) identifies the projected inputs and outputs, namely the canonical variates, as the observed states. We adopt a least squares support vector machine (LS‐SVM) formulation for KCCA, which imposes regularization on the vectors that specify the projections and is amenable to convex optimization. We prove theoretically that, based on the statistical consistency of KCCA, the observed states determined by the proposed state observer has a guaranteed correlativity with the actual states (when properly transformed). Furthermore, such observed states, when supplemented with the information of succeeding inputs, can be used to predict the succeeding outputs with guaranteed upper bound on the prediction error. Case studies are performed on two numerical examples and an exothermic continuously stirred tank reactor (CSTR).

Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data

A novel framework is proposed that utilizes symbolic regression via genetic programming to identify free-form partial differential equations from scarce and noisy data. The framework successfully identified ground truth models for four synthetic systems (an isothermal plug flow reactor, a continuously stirred tank reactor, a nonisothermal reactor, and viscous flow governed by Burgers' equation) from time-variant data collected at one location. A comparative analysis against the so-called weak Sparse Identification of Nonlinear Dynamics (SINDy) demonstrated the proposed framework's superior ability to identify meaningful partial differential equation (PDE) models when data was scarce. The framework was further tested for robustness to noise and scarcity, showing successful model recovery from as few as eight time series data points collected at a single point in space with 50% noise. These results emphasize the potential of the proposed framework for the discovery of PDE models when data collection is expensive or otherwise difficult.

Rapid Start-up of Continuous Autotrophic Nitrogen Removal Reactor by Hybrid-inoculating PN and PN/A Granular Sludges

The rapid cultivation of partial nitritation/ANAMMOX （PN/A） granular sludge in a continuous-flow mode is one of the key technologies for efficient biological nitrogen removal in domestic wastewater treatment. Compared with that in PN/A granular sludge, PN granular sludge demonstrates a shorter incubation period and suitability for batch culture. It is also a good carrier for enriching ANAMMOX （AMX） bacteria. In this study, we established a continuous-flow autotrophic nitrogen removal process in three continuously stirred tank reactors （CSTR） （R1-R3） by hybrid-inoculating PN/A and PN granular sludge at the mass ratios of 3∶1, 1∶1, and 1∶3, respectively. By implementing high ammonium nitrogen loading and short hydraulic retention time, continuous autotrophic nitrogen removal processes were successfully started up in the three CSTRs. The results showed that compared with that of R1 and R2, R3 had a longer start-up time but a similar steady-state nitrogen removal performance. The total nitrogen removal load of R3 could be more than 2.6 kg·（m3·d）-1. Intriguingly, the inoculated PN granular sludge served as a precursor for PN/A granular sludge cultivation. This approach facilitated the enrichment of anaerobic ammonia-oxidizing bacteria （AMX） by introducing abundant ammonium-oxidizing bacteria （AOB） and nitrite nitrogen substrates into the CSTR. According to the results of high-throughput sequencing, the microbial abundance and diversity of the mature granules in R1-R3 were significantly higher than those of the inoculation sludge. AOB （genus Nitrosomonas）, AMX （genera Candidatus Kuenenia and Candidatus Brocadia）, and symbiotic heterotrophs, such as Chloroflexi, Bacteroidetes, and Chlorobi, drove the autotrophic nitrogen removal process and maintained the stability of the granular structure. In summary, a novel start-up strategy of hybrid-inoculating granular sludge was provided for a continuous-flow autotrophic nitrogen removal in engineering application.

Physics-informed neural networks with hard linear equality constraints

Surrogate modeling is used to replace computationally expensive simulations. Neural networks have been widely applied as surrogate models that enable efficient evaluations over complex physical systems. Despite this, neural networks are data-driven models and devoid of any physics. The incorporation of physics into neural networks can improve generalization and data efficiency. The physics-informed neural network (PINN) is an approach to leverage known physical constraints present in the data, but it cannot strictly satisfy them in the predictions. This work proposes a novel physics-informed neural network, KKT-hPINN, which rigorously guarantees hard linear equality constraints through projection layers derived from KKT conditions. Numerical experiments on Aspen models of a continuous stirred-tank reactor (CSTR) unit, an extractive distillation subsystem, and a chemical plant demonstrate that this model can further enhance the prediction accuracy.

Scaling study of miniaturised continuous stirred tank reactors via residence time distribution analysis and application in the production of iron oxide nanoparticles

Magnetically agitated miniaturised continuous stirred tank reactors (mCSTRs) are an attractive platform for the intensification of chemical reactions involving solids by combining active stirring and intensified heat and mass transfer due to their small dimensions. This work investigated the operation of mCSTRs at flowrates up to 60 ml/min (space time of 3 s per tank) as a means of increasing the throughput of fast reactions. Investigation of the residence time distribution under varying operational (flowrate, stirrer rotational speed) and reactor geometrical (stirred volume, stir bar size) parameters, showed deviation from the ideal CSTR behaviour at increasing flowrates, which could be mitigated by keeping the stir bar length close to the tank diameter, increasing stirrer rotational speed, and using larger tank sizes. Assembling mCSTRs into cascades did not amplify non-ideal behaviour and allowed narrowing the residence time distribution at high throughput. Various configurations of mCSTR cascades were evaluated for the synthesis of iron oxide nanoparticles (IONPs) via iron chloride co-precipitation with NaOH, demonstrating the importance of residence time distribution (RTD) control when increasing the throughput of nanoparticle production. Using a 5 × 3 ml mCSTR cascade for the core formation followed by a 5 × 3 ml mCSTR cascade for deagglomeration/stabilisation, the IONP flow synthesis was scaled successfully, producing high quality nanoparticles (7.3 ± 2 nm) at 60.5 ml/min (l/h scale).

Simultaneous state‐estimator tuning and parameter estimation for systems with nonstationary disturbances, multi‐rate data, and measurement delays

AbstractModel‐based monitoring and control of chemical and biochemical processes rely on state estimators such as extended Kalman filters (EKFs) to ensure accurate online model predictions. Accurate predictions depend on appropriate model parameters and suitable state‐estimator tuning factors. Extensions to our previously developed simultaneous parameter estimation and tuning (SPET) method are proposed so that SPET can be used for systems with nonstationary disturbances, time‐varying parameters, multi‐rate data, and measurement delays. A continuous stirred tank reactor (CSTR) case study with simulated data is used to illustrate and test the proposed method. Superior online model predictions and state‐estimator performance are achieved using SPET compared to a traditional approach for parameter estimation and EKF tuning, with improvements in the average sum‐of‐squared prediction errors ranging from 3% to 52% for the scenarios tested. The SPET approach will also be useful for more‐advanced state estimators that require the same tuning information as EKFs.

Event-Triggered Control of Switched Nonlinear Time-Delay Systems With Asynchronous Switching.

This article investigates the event-triggered switching control (ETSC) of switched nonlinear time-delay systems (SNTDSs) with asynchronous switching. First, we study the input-to-state stability (ISS) and integral ISS (iISS) for SNTDSs with asynchronous switching, where switching instants are generated based on the designed event-triggered mechanism. Among existing works on the ETSC, systems behavior at event-triggered instants is neglected. In fact, whenever an event is triggered, the systems mode will jump suddenly such that a switch is imposed to the systems, leading to the change of subsystems. Moreover, asynchronous switching behavior may occur between the actual subsystem and its corresponding controller. These facts bring great challenges for the event-triggered mechanism design and ISS analysis. To tackle these problems, a new Lyapunov-based event-triggered mechanism with adjustable parameters is designed to establish the relationship between systems switches and event triggers, and exclude the Zeno phenomenon. The analysis of the asynchronous switching behavior can be implemented and some ISS and iISS criteria of SNTDSs are derived utilizing the merging switching technique. Finally, two numerical examples, including a practical stirred tank reactor system, are presented to show the validity of the proposed methods.

Pilot-scale evaluation of cascade anaerobic digestion of mixed municipal wastewater treatment sludges.

This work assessed the performance of a pilot-scale cascade anaerobic digestion (AD) system when treating mixed municipal wastewater treatment sludges. The cascade system was compared with a conventional continuous stirred tank reactor (CSTR) digester (control) in terms of process performance, stability, and digestate quality. The results showed that the cascade system achieved higher volatile solids removal (VSR) efficiencies (28-48%) than that of the reference (25-41%) when operated at the same solids residence time (SRT) in the range of 11-15 days. When the SRT of the cascade system was reduced to 8days the VSR (32-36%) was only slightly less than that of the reference digester that was operated at a 15-day SRT (39-43%). Specific hydrolysis rates in the first stage of the cascade system were 66-152% higher than those of the reference. Additionally, the cascade system exhibited relatively stable effluent concentrations of volatile fatty acids (VFAs: 100-120 mg/l), while the corresponding concentrations in the control effluent demonstrated greater fluctuations (100-160 mg/l). The cascade system's effluent pH and VFA/alkalinity ratios were consistently maintained within the optimal range. During a dynamic test when the feed total solids concentration was doubled, total VFA concentrations (85-120 mg/l) in the cascade system were noticeably less than those (100-170 mg/l) of the control, while the pH and VFA/alkalinity levels remained in a stable range. The cascade system achieved higher total solids (TS) content in the dewatered digestate (19.4-26.8%) than the control (17.4-22.1%), and E. coli log reductions (2.0-4.1 log MPN/g TS) were considerably higher (p < 0.05) than those in the control (1.3-2.9 log MPN/g TS). Overall, operating multiple CSTRs in cascade mode at typical SRTs and mixed sludge ratios enhanced the performance, stability digesters, and digestate quality of AD. PRACTITIONER POINTS: Enhanced digestion of mixed sludge digestion with cascade system. Increased hydrolysis rates in the cascade system compared to a reference CSTR. More stable conditions for methanogen growth at both steady and dynamic states. Improved dewaterability and E. coli reduction of digestate from the cascade system.

The effects of drag models and operating condition on the simulation of photocatalytic degradation of pesticides in a fluidized bed reactor.

Fluidized bed reactor can enhance mass transfer and increase reaction rate. Numerical simulation helps to optimize fluidized bed reactors. The present paper models the photocatalytic oxidation of 2,4-dichlorophenoxy acetic acid in a fluidized bed reactor using the Eulerian-Eulerian model. The drag models have influences on the distribution of catalysts particles. The bed expands under the fluid flow and reaches a quasi-steady height at approximately 3s. The asymmetric distribution of catalysts with respect to the axis plane is predicted. The Gidaspow model predicts the nearly same bed expansion with the experimental data, whereas the Syamlal-O Brien model overestimates it. The simulation results at two pH values are in accordance with the experimental data. The removal in the continuous stirred tank reactor decreases with the increase in flow rate.

Au Supported on Bovine-Bone-Derived Hydroxyapatite Catalyzes CO2 Photochemical Reduction toward Methanol

In this work, gold-photo-catalyzed CO2 transformation was conducted and the effect of three variables with two levels was investigated: support (TiO2 and hydroxyapatite from bovine bone (BB)), Au content (5 and 10%) and activation wavelength (254 and 380–700 nm). Reactions were conducted in a stirred tank reactor by bubbling CO2 (9 × 10−3 dm3/min) in 0.1 dm3 of 0.5 M NaOH solution. The catalysts were synthesized using AuCl3, TiO2 and BB. Au nanoparticles were obtained by reduction with Hetheroteca inuloides, thus eliminating calcination and hydrogenation to reduce the gold species. By TEM, the particle size distribution was determined, and the synthesized nanoparticle sizes varied in the range of 9 to 19 nm, depending on the support and Au content. By UV–Vis spectroscopy, the energy band gaps of the prepared materials were 2.18 eV (10% Au/BB), 2.38 eV (5% Au/BB), 2.42 eV (BB), 3.39 eV (5% Au/TiO2), 3.41 eV (10% Au/TiO2) and 3.43 eV for pure TiO2. Methanol and formic and acetic acids were identified during the process. Selectivity toward methanol was found to be improved with the 10% Au/BB catalytic system.

Increasing urine nitrification performance with sequential membrane aerated biofilm reactors

This study aimed to investigate whether separating organics depletion from nitrification increases the overall performance of urine nitrification. Separate organics depletion was facilitated with membrane aerated biofilm reactors (MABRs). The high pH and ammonia concentration in stored urine inhibited nitrification in the first stage and therewith allowed the separation of organics depletion from nitrification. An organics removal of 70 % was achieved at organic loading rates in the influent of 3.7 gCOD d−1 m−2. Organics depletion in a continuous flow stirred tank reactor (CSTR) for organics depletion led to ammonia stripping through diffused aeration of up to 13 %. Using an MABR, diffusion into the lumen amounted for 4 % ammonia loss only. In the MABR, headspace volume and therefore ammonia loss through the headspace was negligible. By aerating the downstream MABR for nitrification with the off-gas of the MABR for organics depletion, 96 % of the ammonia stripped in the first stage could be recovered in the second stage, so that the overall ammonia loss was negligibly low. Nitrification of the organics-depleted urine was studied in MABRs, CSTRs, and sequencing batch reactors in fed batch mode (FBRs), the latter two operated with suspended biomass. The experiments demonstrated that upstream organics depletion can double the nitrification rate. In a laboratory-scale MABR, nitrification rates were recorded of up to 830 mgNL−1 d−1 (3.1 gN m−2 d−1) with ambient air and over 1500 mgNL−1 d−1 (6.7 gN m−2 d−1) with oxygen-enriched air. Experiments with a laboratory-scale MABR showed that increasing operational parameters such as pH, recirculation flow, scouring frequency, and oxygen content increased the nitrification rate. The nitrification in the MABR was robust even at high pH setpoints of 6.9 and was robust against process failures arising from operational mistakes. The hydraulic retention time (HRT) required for nitrification was only 1 to 2 days. With the preceding organics depletion, the HRT for our system requires 2 to 3 days in total, whereas a combined activated sludge system requires 4 to 8 days. The N2O concentration in the off-gas increases with increasing nitrification rates; however, the N2O emission factor was 2.8 % on average and independent of nitrification rates. These results indicate that the MABR technology has a high potential for efficient and robust production of ammonium nitrate from source-separated urine.

Thermodynamics of Growth in Open Chemical Reaction Networks.

We identify the thermodynamic conditions necessary to observe indefinite growth in homogeneous open chemical reaction networks (CRNs) satisfying mass action kinetics. We also characterize the thermodynamic efficiency of growth by considering the fraction of the chemical work supplied from the surroundings that is converted into CRN free energy. We find that indefinite growth cannot arise in CRNs chemostatted by fixing the concentration of some species at constant values, or in continuous-flow stirred tank reactors. Indefinite growth requires a constant net influx from the surroundings of at least one species. In this case, unimolecular CRNs always generate equilibrium linear growth, i.e., a continuous linear accumulation of species with equilibrium concentrations and efficiency one. Multimolecular CRNs are necessary to generate nonequilibrium growth, i.e., the continuous accumulation of species with nonequilibrium concentrations. Pseudounimolecular CRNs-a subclass of multimolecular CRNs-always generate asymptotic linear growth with zero efficiency. Our findings demonstrate the importance of the CRN topology and the chemostatting procedure in determining the dynamics and thermodynamics of growth.

DEVELOPMENT AND ASSESSMENT OF MANUAL AND AUTOMATED PID CONTROLLERS FOR THE OPTIMUM PRODUCTION OF ETHYLENE GLYCOL IN A CSTR

Industrially, one of the techniques used to enhance the optimum production of petrochemicals is the configuration of process control units such as gas chromatography (G.C. Analyzer) for concentration and thermocouple for temperature in the production system. This research focused on the application of the principles of mass and energy conservation in the development of dynamic or unsteady state models for predicting the behaviour of the system or process involved in the production of ethylene glycol from a reaction involving oxidation of ethylene-to-ethylene oxide which further undergoes hydrolysis to produce ethylene glycol in a continuous stirred tank reactor (CSTR). The high mathematical complexities of the mass and energy balance models of the Process as a result of the reaction kinetic scheme of the non-elementary reaction of the process which integrated both linear and non-linear terms into the models were resolved by the application of the principles of Taylor’s series expansion, Laplace Transform, partial fraction and matrix in the development of the dynamic models. Process simulation tool (Simulink) was utilized in the configuration of closed-loop systems with thermocouple and G. C. Analyzer, at the feed point of the reactor for process variables (temperature and concentration) control during the process using proportional, integral and derivative (PID) as controller parameters. The results of the process behaviour showed that manual tuning of different controller parameters experienced a high level of fluctuations and would not attain its stability or steady state even at a processing time above 16 seconds. Whereas, automatic tuning of desired controller gains of 1.361, 1.056 and 0.4109 for K P, K I and K D for temperature and pressure with a tuning time of 143.9 Seconds, requires a maximum time of 8 seconds for the system to attain stability. This study showed that automated tuning of the controller resulted in better performance characteristics for optimum production of ethylene glycol in a non-isotheral continuous stirred tank reactor within the shortest possible time.