Continuous Stirred Tank Reactor Research Articles

Correlations and impact of anaerobic digestion operating parameters on the start-up duration: Database construction for robust start-up guidelines

Anaerobic digestion (AD) has become a popular technique for organic waste management while offering economic and environmental advantages. As AD becomes increasingly prevalent worldwide, research efforts are primarily focused on optimizing its processes. During the operation of AD systems, the occurrence of unstable events is inevitable. So far, numerous conclusions have been drawn from full and lab-scale studies regarding the driving factors of start-up perturbations. However, the lack of standardized practices reported in start-up studies raises concerns about the comparability and reliability of obtained data. This study aims to develop a knowledge database and investigate the possibility of applying machine learning techniques on experimentation-extracted data to assist start-up planning and monitoring. Thus, a standardized database referencing 75 cases of start-up of one-stage wet continuously-stirred tank reactors (CSTR) processing agricultural, industrial, or municipal organic effluent in mono-digestion from 31 studies was constructed. 10 % of the total observations included in this database concern failed start-up experiments. Then, correlations between the parameters and their impacts on the start-up duration were studied using multivariate analysis and a model-based ranking methodology. Insights into trends of choices were highlighted through the correlation analysis of the database. As such, scenarios favoring short start-up duration were found to involve relatively low retention times (average initial and final hydraulic retention times, (HRTi) and (HRTf) of 26.25 and 20.6 days, respectively), high mean organic loading rates (average OLRmean of 5.24 g VS·d−1·L −1) and the processing of highly fermentable substrates (average feed volatile solids (VSfeed) of 81.35 g L−1). The model-based ranking of AD parameters demonstrated that the HRTf, the VSfeed, and the target temperature (Tf) have the strongest impact on the start-up duration, receiving the highest relative scores among the evaluated AD parameters. The database could serve as a reference for comparison purposes of future start-up studies allowing the identification of factors that should be closely controlled.

Optimal Tracking Neuro-Control of Continuous Stirred Tank Reactor Systems: A Dynamic Event-Driven Approach

Optimal Tracking Neuro-Control of Continuous Stirred Tank Reactor Systems: A Dynamic Event-Driven Approach

ANFIS and Takagi–Sugeno interval observers for fault diagnosis in bioprocess system

This paper develops a data-driven approach for incipient fault diagnosis based on ANFIS and Takagi–Sugeno (TS) interval observers. First, the nonlinear bioreactor system is identified using an adaptive neuro-fuzzy inference system (ANFIS), which results in a set of polytopic TS models derived from measurement data. Second, a bank of TS interval observers is deployed to detect sensor and process faults using adaptive thresholds. Unlike other works that require training fault data, only fault-free data is considered for ANFIS learning in this work. Fault insolation is based on residual generation and evaluated on a fault signal matrix (FSM). Parametric uncertainty and measurement noise are considered to guarantee the method’s robustness. The effectiveness of the proposed method is tested on a well-known bioreactor Continuous stirred tank reactor system (CSTR) reference simulator.

Disturbance rejection design for Gaussian process-based model predictive control using extended state observer

Gaussian process (GP) regression has gained significant popularity in machine learning because it has the intrinsic capability to capture uncertainty in function prediction and requires a limited number of hyperparameters to be optimized. In this study, a Gaussian process model predictive control (GPMPC) algorithm is proposed to model the unknown dynamics of the process using Gaussian process regression. The GPMPC incorporates the expected variance of the GP model to account for the model's uncertainty and to achieve prudent control. Meanwhile, the extended state observer (ESO) is introduced for the GPMPC, which can estimate the unmodeled dynamics and unknown disturbance. With the designed feedforward gain, the proposed extended state observer-based GPMPC (GPMPC-ESO) method can achieve offset-free performance. Theoretical analysis is conducted to evaluate the stability and disturbance rejection performance of the control system. Finally, the algorithms are validated by simulation in continuous stirred tank reactor (CSTR) process control.

Enhancing hydrogen generation from sodium borohydride hydrolysis and the role of a Co/CuFe2O4 nanocatalyst in a continuous flow system

In this study, a series of cobalt-based spinel ferrites catalysts, including nickel, cobalt, zinc, and copper ferrites, were synthesized using the sol–gel auto-combustion method followed by a chemical reduction process. These catalysts were employed for accelerating hydrogen generation via the sodium borohydride hydrolysis process. A continuous stirred tank reactor was used to perform catalytic reactor tests. All samples were subjected to analysis using XRD, FESEM, EDX, FTIR, and nitrogen adsorption–desorption techniques. The results revealed that the cobalt-based copper ferrite sample, Co/Cu-Ferrite, exhibited superior particle distribution, and porosity characteristics, as it achieved a high hydrogen generation rate of 2937 mL/min.gcat. In addition, the higher electrical donating property of Cu-Ferrite which leads to the increase in the electron density of the cobalt active sites can account for its superior performance towards hydrolysis of NaBH4. Using the Arrhenius equation and the zero-order reaction calculation, activation energy for the sodium borohydride hydrolysis reaction on the Co/Cu-Ferrite catalyst was determined to be 18.12 kJ/mol. This low activation energy compared to other cobalt-based spinel ferrite catalysts confirms the catalyst's superior performance as well. Additionally, the outcomes from the recycling experiments revealed a gradual decline in the catalyst's performance after each cycle during 4 repetitive cycles. The aforementioned properties render the Co/Cu-Ferrite catalyst an efficient catalyst for hydrogen generation through NaBH4 hydrolysis.

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods.

Numerical Simulation of the Thermal Destruction of Organophosphorous

The organophosphorus class of compounds has several usages nowadays. Some halogenated organophosphorus are used as flame retardants, and others are used as pesticides worldwide. In the last case, those compounds are subject to law regulation. They are now used as chemical weapons in many wars and terrorist attacks. Due to this variety of usages and the high toxicity of such compounds, the study of the proper treatment for residues containing those substances is significant to prevent the degradation of soil, water, and the atmosphere. For these reasons, the present study conducted numeric simulations of the incineration of organophosphorus compounds using kinetic models of combustion and pyrolysis found in the literature. The study of the degradation of this class of substances and the pollutant formation was made by simulating a homogeneous zeroth-order reactor and a continuous stirred tank reactor model. Due to the results of the simulations, the degradation made with the batch mode operation is efficient in the degradation of the substances considered in this work. The stream-containing process products have CO2, CO, HF, HOPO, and HOPO2. The three last cited have no determination about the emission ranges in the legislation, but their concentrations indicate the necessity of treatment for them. Furthermore, it was possible to evaluate the negative influence of the hydrocarbon used as fuel and the presence of CO2 in the degradation of the organophosphate compounds.

Mild Strategy for the Preparation of Alkyl Sulfonyl Fluorides from Alkyl Bromides and Alcohols Using Photoredox Catalysis and Flow Chemistry.

Facile access to sp3-rich scaffolds containing a sulfonyl fluoride group is still limited. Herein, we describe a mild and scalable strategy for the preparation of alkyl sulfonyl fluorides from readily available alkyl bromides and alcohols using photoredox catalysis. This approach is based on halogen atom transfer (XAT), followed by SO2 capture and fluorination. The method features mild conditions enabling fast access to high-value derivatives and has been scaled up to 5 g using a continuous stirred tank reactor cascade.

A Hierarchical approach for isolating sensor faults from un-stealthy attacks in large-scale systems

This paper proposes a novel hierarchical-adaptive threshold abnormal behavior discrimination scheme (HATAD) for Lipschitz nonlinear large-scale systems with disturbances and uncertain dynamics. Two layers of groups of interlinked observers, namely the supervisor and local layers, are designed. The local layer using open loop observers, switching technique, and local controllers is first developed to protect the physical system from the attack effects, where the proposed switching technique can connect the plant to the local controller under the attack conditions. At the supervisor layer, the main controllers and three types of observers are constructed to detect and discriminate the abnormal behaviors and determine the faulty unit. This architecture leads to less restrictive mathematical conditions and analyses, which ensure the stability and the L2−L∞ performance of the estimation error dynamics. To demonstrate the effectiveness of the proposed approach, a simulation study has been conducted on a large-scale system of two units of benchmark continuous stirred tank reactor (CSTR).

Front Cover: Continuous Synthesis of a Macrocyclic Sulfite of Polyethylene Glycol by Cascaded Continuous Stirred Tank Reactors (CSTRs) (Chem. Eur. J. 25/2024)

Front Cover: Continuous Synthesis of a Macrocyclic Sulfite of Polyethylene Glycol by Cascaded Continuous Stirred Tank Reactors (CSTRs) (Chem. Eur. J. 25/2024)

Rapid start-up and operational characteristics of partial denitrification coupled with anammox driven by innovative strategies

The Partial Denitrification-Anammox (PD/A) process established a low-consumption, efficient and sustainable pathway for complete nitrogen removal, which is of great interest to the industry. Rapid initiation and stable operation of the PD/A systems were the main issues limiting its engineering application in wastewater nitrogen removal. A PD/A system was initiated in a continuous stirred-tank reactors (CSTRs) in the presence of low concentration of organic matter, and the effects of organic matter types and COD/NO3−-N ratios on the performance of the PD/A system, and microbial community characteristics were explored. The results showed that low concentrations of organic matter could promote the rapid initiation of the Anammox process and then the strategy of gradually replacing NO2−-N with NO3−-N could successfully initiate the PD/A system at 70 days. The type of organic matter had a significant effect on the initiation of the Anammox and the establishment of the PD/A system. Compared to glucose, sodium acetate was more favorable for rapid start-up and the synergy among microorganisms, and organic matter was lower, with an optimal COD/NO3−-N ratio of 3.0. Microorganisms differed in their sensitivity to environmental factors. The relative abundance of Planctomycetota and Proteobacteria in R2 was 51 %, with the presence of three typical anammox bacteria, Candidatus_Brocadia, Candidatus_Kuenenia, and Candidatus_Jettenia in the system. This study provides a new strategy for the rapid initiation and stable operation of the PD/A process.

Continuous Synthesis of a Macrocyclic Sulfite of Polyethylene Glycol by Cascaded Continuous Stirred Tank Reactors (CSTRs).

Invited for the cover of this issue is the group of Long Pan and co-workers at Asymchem Life Sciences (Tianjin) Co. Ltd. The image depicts a novel continuous process for the synthesis of a macrocyclic poly(ethylene glycol) (PEG) sulfite, the precursor to PEG macrocyclic sulfate, a useful building block in PEG chemistry. Read the full text of the article at 10.1002/chem.202304319.

A compact 3D printed magnetically stirred tank reactor cascade coupled with a free impinging jet for continuous production of colloidal nanoparticles

Most colloidal nanoparticle production processes involve more than one step, such as particle nucleation, growth and stabilisation, that often take place at different time scales. Flow chemistry offers an attractive platform for nanoparticle synthesis, as it can provide excellent control of conditions for each individual step. In this work, a free impinging jet reactor (FIJR), known for its rapid mixing capability and fouling-proof operation, is followed by a cascade of 5 miniaturised (3 ml) continuous stirred tank reactors (mCSTR) that provides longer residence time and efficient temperature control for subsequent synthesis steps, while still being resistant to clogging due to active mixing. The small (0.1 mm) diameter nozzle design allowed the FIJR to provide excellent mixing even at relatively low flowrates (2 ml/min/jet). The mCSTR cascade was arranged in a vertical configuration (one tank on top of another), offering a compact design that is simple to use and requiring only one magnetic stirrer. It provided near-ideal macromixing with limited dead volumes and interconnections, as reflected by a residence time distribution comparable with the theoretical one for a CSTR cascade. Most parts of the reactor system were 3D printed with features that facilitated the assembly. The proposed FIJR design overcomes the accuracy limitations of 3D printing by combining it with micromilling to achieve one of the smallest FIJR reported and by allowing for rotation of the nozzles to easily resolve issues with jet alignment. The reactor system was employed for the synthesis of colloidal (∼10 nm size) silver nanoparticles via NaBH4 reduction of AgNO3, in a 0.06 g/day scale for 50 min continuous operation and (∼6 nm core size) iron oxide nanoparticles via co-precipitation of Fe3+/Fe2+, producing colloidally stable nanoparticles in a 16.1 g/day scale for over 2 h continuous operation.

Model-Free Adaptive State Feedback Control for a Class of Nonlinear Systems

This paper investigates state feedback control for a class of discrete-time multiple input and multiple output nonlinear systems from the perspective of model-free adaptive control and state observation. The design of a dynamic state feedback control can be efficiently carried out using dynamic linearization and state observation. The stability of the proposed method is guaranteed by theoretical analysis. Numerical simulation tests and experimentation on a continuous stirred tank reactor are carried out to validate the effectiveness of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The growth in the scale of factories and the complexity of associated production processes increases the complexity and time involved in associated mathematical modelling. Data driven approaches to control remove the need to model processes. To the best of the authors’ knowledge, existing approaches to model-free adaptive control (MFAC) of general systems are all based on an input-output control paradigm. These methods thus cannot guarantee the stability of the system state. The purpose of this study is to develop a novel Model-Free Adaptive Control (MFAC) approach to achieve control of the system state. In this paper, the assumptions required to achieve model-free adaptive control by state feedback are presented mathematically. A controller design and the associated stability proof are then presented. Numerical simulation and experimentation is conducted to validate the effectiveness of the proposed approach. In future research, state feedback data control in the presence of random disturbances will be investigated.

In-situ methane enrichment in anaerobic digestion of food waste slurry by nano zero-valent iron: Long-term performance and microbial community succession

In this work, nano zero-valent iron (nZVI) was added to a lab-scale continuous stirring tank reactor (CSTR) for food waste slurry treatment, and the effect of dosing rate and dosage of nZVI were attempted to be changed. The results showed that anaerobic digestion (AD) efficiency and biomethanation stability were optimum under the daily dosing and dosage of 0.48 g/gTCOD. The average daily methane (CH4) yield reached 495.38 mL/gTCOD, which was 43.65% higher than that at control stage, and the maximum CH4 content reached 95%. However, under single dosing rate conditions, high nZVI concentrations caused microbial cell rupture and loosely bound extracellular polymeric substances (LB-EPS) precipitation degradation. The daily dosing rate promoted the hydrogenotrophic methanogenesis pathway, and the activity of coenzyme F420 increased by 400.29%. The microbial analysis indicated that daily addition of nZVI could promote the growth of acid-producing bacteria (Firmicutes and Bacteroidetes) and methanogens (Methanothrix).

CO2‐based spouted fluidized bed gasification of kitchen waste: Experimental investigation with process modelling

AbstractIn the present study, kitchen waste is chosen as a solid feed for producing high‐caloric valuable syngas using CO2 as a gasifying agent. Dried kitchen waste is gasified in a spouted fluidized bed reactor for syngas production. In the process, along with syngas, a significant quantity of oil (48 wt.%) and solid char residue is obtained during pyrolysis. Further, the spouted bed process is simulated in ASPEN PLUS using various reactor modules and the calculated results is validated with the experimental data. A model using the combination of continuous stirred tank reactor (CSTR) and plug flow reactor (PFR) with recycling of gas and char fits the behaviour of spouted bed. Further, the experimental investigation of fixed and spouted bed gasification of kitchen showed that the calorific value of syngas obtained during gasification is 10.45 MJ/nm3 with 30.00 vol.% CO and 24.00 vol.% H2. Further, the effect of process parameters such as pressure, temperature, spouting gas flow rate, and feed rate of kitchen waste on overall syngas composition is evaluated using ASPEN PLUS software.

A novel microbial community restructuring strategy for enhanced hydrogen production using multiple pretreatments and CSTR operation

To achieve rapid enrichment of the targeted hydrogen-producing bacterial population and reconstruction of the microbial community in the biological hydrogen-producing reactor, the activated sludge underwent multiple pretreatments using micro-aeration, alkaline treatment, and heat treatment. The activated sludge obtained from the multiple pretreatments was inoculated into the continuous stirred tank reactor (CSTR) for continuous operations. The community structure alteration and hydrogen-producing capability of the activated sludge were analyzed throughout the operation of the reactor. We found that the primary phyla in the activated sludge population shifted to Proteobacteria, Firmicutes, and Bacteroidetes, which collectively accounted for 96.69% after undergoing several pretreatments. This suggests that the multiple pretreatments facilitated in achieving the selective enrichment of the fermentation hydrogen-producing microorganisms in the activated sludge. The CSTR start-up and continuous operation of the biological hydrogen production reactor resulted in the reactor entering a highly efficient hydrogen production stage at influent COD concentrations of 4000 mg/L and 5000 mg/L, with the highest hydrogen production rate reaching 8.19 L/d and 9.33 L/d, respectively. The main genus present during the efficient hydrogen production stage in the reactor was Ethanoligenens, accounting for up to 33% of the total population. Ethanoligenens exhibited autoaggregation capabilities and a superior capacity for hydrogen production, leading to its prevalence in the reactor and contribution to efficient hydrogen production. During high-efficiency hydrogen production, flora associated with hydrogen production exhibited up to 46.95% total relative abundance. In addition, redundancy analysis (RDA) indicated that effluent pH and COD influenced the distribution of the primary hydrogen-producing bacteria, including Ethanoligenens, Raoultella, and Pectinatus, as well as other low abundant hydrogen-producing bacteria in the activated sludge. The data indicates that the multiple pretreatments and reactor's operation has successfully enriched the hydrogen-producing genera and changed the community structure of microbial hydrogen production.

Mass transfer phenomena of partially miscible liquids under liquid-liquid slug flow in a circular microchannel

Microreactors have been demonstrated to become an effective tool for increasing mass and heat transfer in heterogeneous chemical processes that are unachievable in batch or continuous stirred tank reactors. The key to superior performance is the availability of a large specific surface area (A/V) in the microsystem for mass and heat transfer between phases. Among the flow patterns generated in the two-phase liquid-liquid flow, slug is an ideal flow with a high stability characteristic, regular velocity, and uniform dimension throughout a microchannel system. The flow behavior gives slugs great potential to increase mass and heat transfer between phases, so optimizing the utilization of microreactors in many chemical process applications needs an in-depth understanding of the liquid-liquid hydrodynamic. Therefore, the current work aims to determine the mass transfer coefficient under liquid-liquid slug flow and the influence of slug dimensions, channel material, volumetric flow rate, and the flow rate ratio of the organic-aqueous phase on the mass transfer coefficient. The experiments were performed in 1 mm (ID) circular PTFE and silicone tubes, with a 30–40 ml/hour flow rate, and sodium hydroxide concentrations of 0.1, 0.13, and 0.15 M were used. The results exhibit that the most prominent overall mass transfer coefficient was 0.121/s, attained at 0.15 M sodium hydroxide concentration, with 40 ml/hour total discharge within the silicone channel. Microchannels generated smaller slug sizes at a higher aqueous phase flow rate than the organic phase. The smaller size provided a large specific surface area (A/V) for enhancing the mass transfer coefficient.

A temporal discretization and spatial integration SCR model with dual temperature-related parameters

Developing an accurate and rapid Selective Catalytic Reduction (SCR) model is crucial for advanced control strategies, which is challenging due to the complex physicochemical processes. In this paper, a novel control-oriented SCR model is proposed, which exhibits faster and more accurate estimation performance for both NOx and NH3 emissions. Unlike traditional modeling methods, this research develops a temporal discretization and spatial integration (TDSI) SCR model with dual temperature-related parameters. By introducing NH3 recycling, the spatial integration model is improved, leading to an enhanced accuracy in model estimation. Moreover, A time discretization method based on current state can effectively address the issue of sampling time sensitivity. Additionally, dual temperature-related parameters can significantly enhance model accuracy, particularly in high-temperature regions. A simplified and clear flowchart is provided for parameter identification based on a limited number of engine bench test results. In a cold-start World Harmonized Transient Cycle (WHTC), the TDSI model can achieve NOx and NH3 accuracies of 92.26% and 92.52%, respectively, with a computation time of 1.47 s. Through comprehensive comparisons, the proposed model exhibits comparable accuracy to the GT model while significantly reducing computation time by 98.39%. Additionally, compared to the Continuous Stirred-Tank Reactor (CSTR) and θT models, there is an approximate 3% improvement in NOx and NH3 accuracies. This TDSI model provides a foundation for designing urea injection control strategies and offers new perspectives for simplifying the model design.

Development of a fault diagnostics and tolerance system: An application to continuous stirred tank reactor

Fault diagnosis and tolerance are crucial for monitoring system health and ensuring stability in industrial processes. Challenges arise in designing fault diagnostic solutions for real-time industrial processes with inherent nonlinear dynamic behaviors, particularly when dealing with multiple operating regions characterized by varying dynamics. This article addresses this challenge and proposes a fault diagnostic and tolerant control scheme for industrial systems. The proposed approach integrates a fuzzy-based realization technique with a subspace-aided methodology to effectively handle the nonlinear dynamic behavior observed across different operational scenarios. A practical solution is presented, significantly reducing the computational burden associated with online diagnostics, as the parity vectors are computed offline using available input–output data for different operating regions. During online diagnostics, only computed parity spaces are used with fuzzy realizations for residual generation, leading to a significant reduction in online computation. Numerical examples demonstrate the effectiveness of the proposed method, achieving a high precision rate in fault diagnostics. Furthermore, the diagnostic methodology is integrated with fault-tolerant control for practical applications, as demonstrated in the application of a continuous stirred tank reactor. This integration enables the system to effectively tolerate faults and ensure sub-optimal operation of the industrial process.