Continuous Stirred Tank Reactor Research Articles

Adaptive command filtered control for a class of nonlinear time delay systems with extreme learning machine

AbstractIn this paper, an extreme learning machine (ELM)‐based adaptive command filtered control method is investigated for a class of strict feedback nonlinear systems with unknown external disturbances and time delay. To begin with, the original system state equation is transformed into a new form on account of coordinate transformation. Subsequently, an ELM is adopted to approximate unknown functions which exist in the whole system states, without any prior knowledges of the ideal weight vectors and approximation errors. Secondly, a command filter is developed for the system under consideration, which can avoid the probem of “explosion of complexity” caused by repeated derivation of virtual control signals in traditional backstepping control. Meanwhile, error compensation signals are designed to conquer the shortcoming of a dynamic surface control (DSC) method. The combination of ELM and command filter technique has been used to construct corresponding controllers and adaptive laws. Effective handling of the impact of time delay terms is ensured through designing a novel Lyapunov–Krasovskii functional. The proposed strategy guarantees boundedness of all signals in the closed‐loop system and the tracking error asymptotically converges to a compact set around the origin. In the end, two continuous stirred tank reactors (CSTRs) are taken as example to further verify the efficiency of the put forward control method.

Feeding frequency efficacy on biogas yield of oily substrate anaerobic digestion in continuous stir tank reactor

ABSTRACT Anaerobic treatment of oily substrate, known as grease trap waste (GTW), was investigated for its practicability via continuous stirred tank reactor (CSTR) at different operating conditions and selected recovery strategies of feeding frequency efficacy. This study determine the performance of feeding frequency efficacy, namely feeding every 24 hours (R24H) and feeding every 12 hours (R12H). Under organic loading rate (OLR) of 2.2 gCOD/L.day, R12H exhibited methane composition of 57%, methane production rate of 0.27 LCH4/L.day, and methane yield of 0.14 LCH4/gCODremoved. At the same OLR, R24H recorded methane composition of 60%, methane production rate of 0.29 LCH4/L.day and similar methane yield as R12H. Findings indicated that R24H showed performance comparable to that of R12H. Given minor variation observed in performance, it is recommended that plant operators may consider scheduling 2 times feeding per day for low loading conditions and switch to 1 time feeding per day for higher loading conditions. This strategy is designed to balance the system and prevent shock loads, which could lead to plant shutdowns. This mechanism will induce their conversion to volatile fatty acids (VFAs); thus, reducing the risk of acid accumulation and pH drops, which could inhibit methanogens to produce methane, especially for oily substrate.

Robust sliding-mode observer for unbounded state nonlinear systems

Designing a full-state observer for nonlinear systems has always been accompanied by challenges and restrictive constraints. Mainly, applying a state observer in nonlinear systems with non-minimum phase characteristics is more challenging when the limiting constraints are not satisfied due to diverging internal dynamics. In this paper, a robust sliding-mode observer approach has been successfully employed to estimate the states of nonlinear systems with unbounded and diverging dynamics. The design principles of this observer are based on applying a classifying algorithm in single-input single-output and multiple-input multiple-output nonlinear systems. It is noteworthy that this observer is highly robust against disturbance, uncertainty and measurement noise, and its conditions are less conservative compared to previous nonlinear sliding-mode observers. One novel feature of the proposed observer is that while the system's state gets unbounded and diverged in fault-occurring scenarios or critical circumstances, this observer retains accuracy. The efficiency of the proposed observer is verified in the simulation results for two nonlinear industrial systems, including a hydro-turbine power generation plant and a continuous stirred tank reactor.

A Single-Neuron-Based Temperature Control of a Continuous Stirred Tank Reactor

A Single-Neuron-Based Temperature Control of a Continuous Stirred Tank Reactor

Data-driven economic MPC with asymptotic stability and strong duality verification using Hankel matrix

We consider the problem of dynamic regulation with an economic cost function to control unknown linear systems, in which improving the economic performance and guaranteeing the stability of economical optimal equilibrium point are control objectives. A data-driven economic MPC scheme is presented using measured input-output trajectories without a prior system identification step. Our method uses Hankel matrices which include one input-output data trajectory for prediction in economic MPC, while persistently exciting of the input generating the data is needed. One of the novelties of the presented framework is directly verifying the strong duality property from input-output trajectory with the general cost function, considered as the supply rate. This is used to find a Lyapunov function for data-driven economic MPC. Under the strong duality assumption, asymptotic stability of the economical optimal equilibrium point for the closed-loop system with terminal equality constraint is guaranteed. The proposed data-driven economic MPC approach needs only persistently exciting data trajectory along with an upper bound on the system order and need no model description and no online parameter estimation. The proposed scheme applicability compared to the existing model-based economic MPC and data-driven MPC is illustrated for continuous stirred tank reactor (CSTR) and a numerical example and the robustness of the proposed scheme is evaluated in the case of measurement noise, as well as nonlinear model for CSTR system.

Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process

Unexpected accidents and events in industrial chemical processes have resulted in a considerable number of causalities and property damage. Safety process management in industrial chemical processes is critical to avoid and ensure casualties and property damage. However, due to the immense scope and high complexity of current industrial chemical processes, the traditional safety process management approaches cannot address these challenges to attain adequate fault detection accuracy. To address this issue, an innovative machine learning-based distributed canonical correlation analysis-dynamic independent component analysis (DICA-DCCA) approach is needed to improve the fault detection effectiveness of complicated systems. The (DICA-DCCA) model could potentially detect anomalies and faults in industrial chemical data by utilizing three essential statistics:Id2,Ie2and squared prediction error (SPE). The practical effectiveness of the proposed frameworks is evaluated and compared using a continuous stirred tank reactor (CSTR) framework as a standard benchmark study. The research findings present that the suggested (DICA-DCCA) approach is more resilient and effective in detecting abnormalities and faults than the ICA and DICA approaches with FDR 100 % and FAR 0 %. The implied research approach is robust, operational, and productive.

Modular Cascade of Flow Reactors: Continuous Flow Synthesis of Water-Insoluble Diazo Dyes in Aqueous System.

Continuous flow synthesis is pivotal in dye production to address batch-to-batch variations. However, synthesizing water-insoluble dyes in an aqueous system poses a challenge that can lead to clogging. This study successfully achieved the safe and efficient synthesis of azo dyes by selecting and optimizing flow reactor modules for different reaction types in the two-step reaction and implementing cascade cooperation. Integrating continuous flow microreactor with continuous stirred tank reactor (CSTR) enabled the continuous flow synthesis of Sudan Yellow 3G without introducing water-soluble functional groups or using organic solvents to enhance solubility. Optimizing conditions (acidity/alkalinity, temperature, residence time) within the initial modular continuous flow reactor resulted in a remarkable 99.5% isolated yield, 98.6% purity, and a production rate of 2.90 g · h-1. Scaling-up based on different reactor module characteristics further increased the production rate to 74.4 g·h-1 while maintaining high yield and purity. The construction of this small 3D-printing modular cascaded reactor and process scaling-up provide technical support for continuous flow synthesis of water-insoluble dyes, particularly high-market-share azo dyes. Moreover, this versatile methodology proves applicable to continuous flow processes involving various homogeneous and heterogeneous reaction cascades.

Upgrading biogas plants to produce microbial proteins for aquaculture feed

As the world population continues to grow, alternative protein sources must be sought to meet increased protein needs and avoid the detrimental effects of traditional protein production. Microbial proteins (MP) are a low-carbon footprint alternative suitable for manufacturing food and feed. Bacterial-based MP can be produced at moderate costs using agro-waste, which is already utilised by anaerobic digestion (AD) plants to produce biogas and fertilisers. At present, there is the opportunity to turn existing AD plants into biorefineries for bio-based products like MP destined for fish feed. In this work, we evaluated the feasibility of biogas production coupled with MP production in the same medium-sized European biogas plant through mass and energy balances and basic economic assessment. The liquid phase rich in volatile fatty acids resulting from agro-waste fermentation was used to obtain MP by mixed cultures in a continuous stirred tank reactor, with the highest VSS productivity (1.21 g/L day) obtained at hydraulic retention times of 2-3 days. The obtained MPs were rich in proteins (74.0% of TVS), PHAs (22.5%) and essential amino acids, usable as feed ingredients in aquaculture. An initial economic analysis showed that the production of 590 tMP/year could yield a break-even price of 1,300 €/t of MP, taking into account the investments in the new technology and additional operating costs. This analysis illustrates the competitiveness of a combined plant when evaluated against established market benchmarks and highlights a promising prospect for the expansion of MP production capacity.

Insights into the effects of exogenous folate on the recovery of SPNA system after starvation: Performance, microbial community and metabolism

To investigate the effects of folate addition on the recovery process of the single-stage partial nitritation-anammox (SPNA) system, two continuous stirred tank reactors (CSTRs), named R1 (adding folate) and R2 (control group), were operated for 105 days at room temperatures. The highest nitrogen removal rate (NRR) observed in R1 was 0.47 kg N/(m3·d) with the addition of 3.0 mg/L folate. Additionally, the activities of HZO and HZS in R1 were higher than those in R2. The relative abundance of Candidatus Brocadia reached 23.7% in R1 and 18.2% in R2. Moreover, the relative abundance of nitrogen metabolism genes and carbon metabolism genes increased with folate addition. However, 3.0 mg/L folate addition reduced the relative abundance of nirK/S and the activity and growth of ammonia-oxidizing bacteria, which was not conducive to continually improving the NRR. This study provided a novel perspective on improving the activity and abundance of anammox bacteria during the restoration process.

Polymeric surfactants with high acid values for emulsion type pressure-sensitive adhesives

Low molecular weight polymers with hydrophobic-carboxyl groups have been usually used as anionic surfactants in emulsion polymerization for coatings. However, such polymeric surfactants usually have low acid values below 200, because it is difficult to make high acid-value polymeric surfactants using a normal batch reactor. In this work, we polymerized high acid value polymeric surfactant having high acid values successfully up to 380 using CSTR (continuous stirring tank reactor). The synthesized polymeric surfactant was a random copolymer of P(BA-AA) and we applied them as anionic surfactants in making emulsion-type acrylic PSAs (Pressure-Sensitive Adhesives) to investigate the effects of high acid value on the adhesive properties. This CSTR enabled effective control of the polymerization reaction because it could control the composition and molecular weight of polymeric surfactants through varying monomer ratios, retention time, and initiator concentration. The surface tension analysis and emulsion stability test indicated that the polymeric surfactants exhibited a similar level of critical micelle concentration (CMC) and emulsion stability with other conventional anionic surfactants. Also, the adhesion property analysis showed that the PSA used a high acid value polymeric surfactant resulting in superior adhesive properties and heat resistance than the PSA used both low acid value polymeric surfactant and conventional low molecular weight surfactant. It was very notable that polymeric surfactants having unusually high acid values above 300 because of their high contents of carboxylic acid groups could result in superior adhesive properties and heat resistance.

Single-Particle Elemental Analysis of μm-Sized Battery Materials by Laser Ablation Inductively Coupled Plasma Mass Spectrometry

Lithium-nickel-manganese-cobalt-oxides (NCMs) represent a preeminent class of cathode active materials for employment in commercially available lithium-ion battery applications. To realize large-scale production of the respective NCM precursors, coprecipitation is often conducted in continuous stirred-tank reactors (CSTR). However, precursors coming from CSTR processes show broad particle size distributions and undesired compositional differences within and between particles. To quantify this phenomenon, it is necessary to develop analytical tools to access quantitative data on particle composition depending on their size. Here, we demonstrate a novel application of single particle laser ablation-inductively coupled plasma-mass spectrometry to determine particle size-dependent elemental compositions by exemplary investigation of CSTR-based NCM precursors. An enrichment of Ni in larger particles with a concomitant enrichment of Co and Mn in smaller particles was identified. This compositional differences between particles persisted after calcination of the precursors with lithium hydroxide. The observed particle size-dependent concentration differences by spLA-ICP-MS were validated by scanning electron microscopy with energy-dispersive X-ray analysis.

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.

Data-driven auto-tuning strategy for RTO-MPC based on Bayesian optimization

Real-time optimization (RTO) and model predictive control (MPC) are extensively employed in industrial processes to enhance economic objectives. However, the tuning of the system remains a challenge, in particular, for cases where an explicit model relating the RTO objective and the process dynamics is unknown. To address this issue, an online data-driven auto-tuning strategy leveraging two Bayesian optimization (BO) techniques is proposed. This strategy is useful for situations where the RTO objective and the process dynamics are detectable but their exact functional forms are unclear. The proposed strategy views the RTO objective as a black-box objective and interprets the steady-state conditions as black-box equality constraints within the RTO layer. In this context, the upper-trust-bound based constrained BO (UTB-CBO) method is adopted to optimize the setpoints and enhance solution feasibility. Additionally, the proposed approach can take into account measurable disturbance inputs explicitly and account for their consequential influence on objective optimization by considering disturbance variations as contextual information. Simultaneously, another contextual BO scheme is implemented to automatically tune the MPC controller parameters for improving tracking performance upon accepting the setpoints optimized by the RTO. Simulation results based on a continuous stirred-tank reactor system are given to illustrate the effectiveness of the proposed 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).