Continuous Stirred Tank Reactor System Research Articles

Continuous cultivation of Debaryomyces hansenii (LAF‐3 10 U) on dodecane in synthetic desalter effluent at varying dilution rates on dodecane

AbstractDesalter effluent (DE) is typically discharged into a petroleum wastewater treatment plant, but its high salt concentration deteriorates the biological treatment. This study used various dilution rates to investigate the treatment of a synthetic DE containing dodecane under saline conditions using a halotolerant yeast, Debaryomyces hansenii, to determine the optimum substrate concentration for use in continuous stirred‐tank reactors (CSTRs). A literature review indicated that this study was the first to examine the biological treatment of DE using D. hansenii in a CSTR system. At a low dodecane substrate concentration, DE did not inhibit D. hansenii growth, and the experimental data approached the Monod model, with μmax and Ks selected as 0.08 h−1 and 1575 mg L−1, respectively. The optimum removal of chemical oxygen demand (95.7% and 85%) was obtained at dilution rates of 0.007 and 0.026 d−1. Using D. hansenii in a CSTR system appeared to be a sustainable approach for the biological treatment of DE. Scale‐up of these laboratory findings to the industrial scale is required to confirm that petroleum DE can be treated using equalization and filtration tanks as a continuous bioreactor. Adjusting the dilution rate can provide sufficient time for biodegradation and hydrocarbon removal from high salt DE by halotolerant yeasts like D. hansenii.

Dynamic Event-Triggered Adaptive NN Control for Switched Uncertain Nonlinear Systems.

This article is concerned with the problem of dynamic event-triggered adaptive neural network (NN) control for a class of switched strict-feedback uncertainty nonlinear systems. A novel switched command filter-based dynamic event-triggered adaptive NN control approach is set up by exploiting the backstepping and command filter and the common Lyapunov function method. Since adaptive controllers of subsystems are event triggered, then if the switching happens between any two consecutive triggering instants, asynchronous switching will arise between candidate controllers of subsystems and subsystems. Unlike the existing literature, where maximum asynchronous time is restricted, without any strict limitations on maximum asynchronous time being needed in this article, the asynchronous switching problem is directly handled by proposing a novel switching dynamic event-triggered mechanism (DETM) and event-triggered adaptive controllers of subsystems. Moreover, a piecewise constant variable is introduced into the switching DETM, which overcomes the difficulty of switched measurement error being discontinuous. Also, a strictly positive lower bound of interevent times is obtained. Finally, a continuous stirred tank reactor system and a numerical example are presented to demonstrate the effectiveness of the developed approach.

Computing control invariant sets of cascade nonlinear systems: Decomposition and distributed computing

In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems. The proposed algorithm exploits the structure of the interconnections within a process network. First, the overall system is decomposed into several subsystems with overlapping states. Second, the control invariant set for the subsystems are computed in a distributed manner. Finally, an approximation of the control invariant set for the overall system is reconstructed from the subsystem solutions and validated. We demonstrate the efficacy and convergence of the proposed method to the centralized graph-based algorithm using several numerical examples including a six dimensional continuous stirred tank reactor system.

A novel learning-based asynchronous sliding mode control for discrete-time semi-Markov jump systems

In this paper, a novel asynchronous sliding mode-based learning control is proposed for a class of discrete-time semi-Markov jump systems, in which the probability density function of sojourn-time is dependent on two consecutive modes. The asynchronous switching characteristic is employed for the nonsynchronization between the controller and the jump modes. Based on the discrete-time semi-Markov kernel and multiple-Lyapunov functions, sufficient conditions are provided to guarantee the σ-error mean-square stability of the sliding mode dynamics. Furthermore, a recursive sliding mode learning controller is designed such that the sliding modes can be driven onto the designed sliding surface and the chattering caused by the asynchronous switching and mode jump can be effectively suppressed. Finally, numerical simulations on the continuous stirred tank reactor system are given to demonstrate the effectiveness and potential of the proposed techniques.

Towards automated hazard analysis for CPS security with application to CSTR system

Hazard analysis to identify risks due to cyber attacks is crucial to ensure cyber–physical systems’ safety and correct their behavior. This study proposes a framework to analyze cyber–physical systems (CPS) risks using system dynamics to identify the critical physical components and hazardous interactions. We divide the input space into three regions, namely safe, alarm, and hazard zones, based on some given information and the dynamics of the CPS. These regions show how by manipulation of the critical inputs system can be driven to some predefined region. Moreover, using the concept of gap metric, we refine the list of the input value of vulnerable components to update the regions. Also, as a part of hazard analysis, we study and obtain the time needed to operate the system from a safe region to a hazard region by manipulating the inputs. The proposed framework uses the physical system’s model dynamics to simulate safe, alarm, and hazard mode activity. This framework’s primary goal is to provide decision-makers with a framework to evaluate CPS safety measures to implement adequate safety mechanisms. Model dynamics of continuous stirred tank reactor system (CSTR) has been employed to simulate the process under different manipulation of the input signal based on the proposed framework to identify the critical inputs and hazardous scenarios.

An Improved Particle Swarm Optimization with Multiple Strategies for PID Control System Design

In this paper, an improved particle swarm optimization (PSO) with multiple subpopulations is developed for PID control system designs. The original single population needs to be divided into several subpopulations, and each subpopulation then tackles a corresponding performance index of the system. Under this proposed structure, several PID controllers can be simultaneously designed to meet different performance indexes when the algorithm is executed only one time. It is a great improvement because the general PSO algorithm with a single population can only deal with one performance index. To demonstrate the feasibility of the proposed scheme, a complicated chemical nonlinear process called the continuously stirred tank reactor (CSTR) is illustrated. Three different kinds of control operations are simulated including the step response control, set-point tracking control, and unstable equilibrium point control. For each control case five different performance indexes are assigned to guide the PID controller design combined with the nonlinear CSTR system. Simulation results will sufficiently confirm the superiority of the proposed algorithm.

Incipient fault detection for dynamic chemical processes based on enhanced CVDA integrated with probability information and fault-sensitive features

Recently, canonical variate dissimilarity analysis (CVDA) has emerged as an efficient incipient fault monitoring method for dynamic chemical processes. Nevertheless, the basic CVDA method omits the mining of the probability information hidden in the canonical variate vectors and does not make full use of the fault-sensitive features provided by the prior fault data. Aiming at these two limitations, this paper presents an enhanced CVDA method, called fault-sensitive probability-related CVDA (FSPRCVDA), for better monitoring incipient faults. Firstly, the traditional CVDA is improved to the probability-related CVDA (PRCVDA) by applying the Kullback–Leibler divergence to measure the probability distribution changes of canonical variates. Secondly, fault-sensitive features are extracted by local Fisher discriminant analysis on the normal and prior fault data. The extracted fault-sensitive features are used to construct the auxiliary model for assisting the primary PRCVDA model, which leads to the holistic FSPRCVDA model. The case study on a continuous stirred tank reactor system shows that the proposed FSPRCVDA method has higher incipient fault detection rate than the basic CVDA method.

Event-Triggered Optimal Control for the Continuous Stirred Tank Reactor System

Continuous stirred tank reactor (CSTR) is essential equipment found in chemical processing industries. Control of CSTR process has long been a challenging issue due to the high complexity and strong nonlinearity of the chemical process. In this paper, a novel event-triggered (ET) adaptive dynamic programming (ADP) optimal control (OC) algorithm is developed for continuous stirred tank reactor (CSTR) system. In order to reduce the computational load and the communication data between the controller and the actuator, a new event-triggering condition based on Taylor series expansion is designed for the ET controller. The control policy does not update unless the event-triggering condition is not satisfied. Compared with the time-triggering mechanism, the update frequency of the controller can be obviously reduced. Then, an identifer-actor-critic structure is developed to implement the ET ADP controller. In particular, to overcome the challenge of establishing an exact dynamic for CSTR system, an identifier neural network (NN) is employed to reconstruct the unknown system dynamic based on offline data. Furthermore, the actor-critic structure is developed to obtain the ET control law and the value function. In actor and critic NNs, weights are turned just at the triggered instant and remained constant during the inter event times. Finally, the developed ET ADP controller is applied to the CSTR system. Experimental results show that the developed ET approach can cut down the update frequency to 66\%, which is very significant for the real CSTR system.

Resilient Control of Networked Switched Systems Against DoS Attack

This article investigates the resilient control problem of networked switched systems against denial-of-service attack. The resilient control strategies include the security game of the network layer and the optimal control for the physical layer. First, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{N}$</tex-math></inline-formula> -coalition noncooperative game is utilized to characterize the network security game between an intrusion detection system and the attacker, where their strategy sets are obtained from the Nash equilibrium. Then, by the dynamic programming principles, the optimization strategy is developed to obtain the optimal controller gains for the subsystems. Meanwhile, for ensuring the optimality of the switched system, a genetic algorithm is adopted to search for the optimal switching signal. Finally, the proposed methods are applied to a networked continuous stirred tank reactors system, which demonstrates its effectiveness.

Performance Monitoring of Anaerobic Digestion at Various Organic Loading Rates of Commercial Malaysian Food Waste.

Application of anaerobic digestion (AD) has become common in treating palm oil mill effluent in Malaysia; however, employing AD in treating the organic fraction of municipal solid waste (OFMSW), especially food waste, is still scarce. This study aims to characterize the commercial Malaysian food waste (CMFW) and determine its potential as sustainable bioenergy feedstock through biogas production. The sample was digested via the biomethane potential (BMP) test with the variation of organic loading rates (OLRs), ranging from 0.38 to 3.83 gCOD/L. day, under mesophilic conditions. The digestion process was further evaluated in continuous operation using a 6-L continuous stirred-tank reactor (CSTR). The kinetic properties of the process were also determined. It was found that the CMFW had a significant amount of chemical oxygen demand of 230 g/L and an acidic pH of 4.5 with the carbon to nitrogen (C/N) ratio at 121:1. A maximum methane composition of 81% was obtained at 1.92 gCOD/L in the BMP test with specific methane production (SMP) at 0.952 L. CH4/L.COD fed. The biogas production was well-fitted with the modified Gompertz model with R 2 at 0.9983 and the maximum biogas potential production rate at Rm 0.1573 L/day, whereas in the CSTR operation, a maximum methane composition of 85% was produced at OLR 6 gCOD/L. day with the SMP of 1.13 L. CH4/L.COD fed. The CSTR system was in high stability as the pH was maintained in a range of 6.6–6.7, with an alkalinity ratio of 0.28. This study indicates the CMFW is a sustainable feedstock for biogas production in Malaysia. Toward a circular economy approach, the authorities shall introduce commercial scale CMFW AD as part of managing municipal solid waste issues in Malaysia.

Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis.

The chemical process industry has become the backbone of the global economy. The complexities of chemical process systems have been increased in the last two decades due to online sensor technology, plant-wide automation, and computerized measurement devices. Principal component analysis (PCA) and signed directed graph (SDG) are some of the quantitative and qualitative monitoring techniques that have been widely applied for chemical fault detection and diagnosis (FDD). The conventional PCA-SDG algorithm is a single-scale FDD representation origin, which cannot effectively solve multiple FDD representation origins. The multiscale PCA-SDG wavelet-based monitoring technique has potential because it easily distinguishes between deterministic and stochastic characteristics. This study uses multiscale PCA-SDG to detect, diagnose the root cause and identify the fault propagation path. The proposed method is applied to a continuous stirred tank reactor system to validate its effectiveness. The propagation route of most process failures is detected, identified, and diagnosed, which is well-aligned with the fault description, demonstrating a satisfactory performance of the suggested technique for monitoring the process failures.

Fuzzy event-triggered control for nonlinear networked control systems

This paper is focus on an event-triggered control design problem for nonlinear networked control systems with missing data and transmission delay in the interval type-2 (IT2) fuzzy form. An event-triggered controller is presented under a sampled-state-error mechanism. By dividing the event-triggered interval into some subsets, stability analysis is carried out based on Lyapunov-Krasovskii functional (LKF), and the stability of the closed-loop system is ensured. The proposed design is applied to the continuous stirred tank reactor system (CSTR) and the manipulator system. The control strategy is effective, and the merit of the event-triggered mechanism is indicated.

Fault Detection for Non-Gaussian Stochastic Distribution Systems Based on Randomized Algorithms

This paper presents a new fault detection scheme for stochastic distribution systems with non-Gaussian variables based on a probabilistic framework. The available information is the output probability density function (PDF) rather than the measured outputs themselves. The square-root B-spline function is utilized to formulate the output PDFs. Probabilistic parameter models are developed to characterize system uncertainties and faults. An observer is designed to detect the multiplicative and additive faults. The randomized algorithm is adopted to design the threshold to achieve an optimal balance between the false alarm and fault detection rate. The effectiveness of the proposed method is demonstrated and compared against existing work by utilizing a continuous stirred tank reactor system.

Energy generation from palm oil mill effluent: A life cycle cost-benefit analysis and policy insights

Malaysia's renewable energy policies do not cover production from waste, including the generation of biogas from palm oil mill effluent. This paper combines life cycle cost-benefit analysis (LCCBA) and the analytical hierarchy process (AHP) to provide new insights into costs and benefits of technologies over the life cycles, and on the basis of this information, asks experts to rank different options to enhance policy. The results show that the continuous stirred tank reactor has a higher LCC of 0.63 Million USD/year, compared to a LCC value of 0.55 Million USD/year for the covered lagoon bio-digester. In terms of cost-benefit, the continuous stirred tank reactor has a higher net present value of 0.46 Million USD/year, higher return on investment of 10.11% and a shorter payback period of 9.9 years compared to the covered lagoon bio-digester system, which has a net present value of 0.22 Million USD/year, return on investment of 7.79% and a payback period of 12.8 years. The continuous stirred tank reactor system therefore emerges as more economically feasible compared to the covered lagoon bio-digester system. On providing this information to experts using AHP, the three top ranked policy options emerged as: i) providing detailed environmental guidelines, ii) standardising technical guidelines for biogas installation and iii) covering the open pond wall using lining. Economic insights and policy opportunities based on this research can be used to inform policy decision making in multiple contexts where biogas plant projects are under consideration, in both Malaysia and globally.

Mass balance and kinetics of biodegradation of endocrine disrupting phthalates by Cellulosimicrobium funkei in a continuous stirred tank reactor system

This study investigated the potential ofCellulosimicrobium funkeifor degrading dimethyl phthalate (DMP) and diethyl phthalate (DEP). Effect of different initial concentrations of phthalates on their biodegradation and growth ofC. funkeiwas examined using shake flasks and a continuous stirred tank reactor (CSTR). Complete degradation of both DMP and DEP was achieved in CSTR, even up to 3000 and 2000 mg/L initial concentrations, respectively. Simultaneous degradation of the phthalates in mixture, i.e. more than 80% and 55% biodegradation efficiency were achieved at 1000 and 2000 mg/L initial concentrations of DMP and DEP, respectively, using the CSTR. Mass balance analysis of the degradation results suggested proficient degradation of DMP and DEP with biomass yield values of 0.64 and 0.712, respectively. The high values of inhibition constant Kiestimated using the Tessier and Edward substrate inhibition models indicated very good tolerance ofC. funkeitoward biodegradation of DMP and DEP.

Insight into the role of charge carrier mediation zone for singlet oxygen production over rod-shape graphitic carbon nitride: Batch and continuous-flow reactor

As a new approach of creating the photo-exited electron (e-) and hole (h+) mediation zone for highly selective singlet oxygen (1O2) production, the rod-type graphitic carbon nitride (NCN) has been synthesized from the nitric acid-modified melamine followed by the calcination. The NCN exhibited a higher surface area and surface oxygen adsorption ability than bulk graphitic carbon nitride (BCN). The increment of CO and NHx groups on NCN corresponded to e- and h+ mediation groups, respectively, resulting in higher production of 1O2 than BCN. Moreover, those mediation groups on NCN result in higher recombination efficiency and longer e- decay time. As a result, the optimized NCN-0.5 (derived from 0.5 M of nitric acid-modified melamine) displayed 5.8 times higher kinetic rate constant of atrazine (ATZ) removal under UVA-LED irradiation compared to BCN. This study also evaluated the ATZ degradation pathways and toxicity effect of by-products. In addition, continuous flow experiments using NCN-0.5 showed superior ATZ removal performance with a hybrid concept between a slurry photocatalysis and a continuous stirred tank reactor system using actual effluent obtained from a wastewater treatment plant. Thus, this work provides an insight into the strategy for highly selective 1O2 production and the potential for water purification application.

Adaptive Neural Control and Modeling for Continuous Stirred Tank Reactor with Delays and Full State Constraints

In this paper, an adaptive neural network control method is described to stabilize a continuous stirred tank reactor (CSTR) subject to unknown time-varying delays and full state constraints. The unknown time delay and state constraints problem of the concentration in the reactor seriously affect the input-output ratio and stability of the entire system. Therefore, the design difficulty of this control scheme is how to debar the effect of time delay in CSTR systems. To deal with time-varying delays, Lyapunov–Krasovskii functionals (LKFs) are utilized in the adaptive controller design. The convergence of the tracking error to a small compact set without violating the constraints can be identified by the time-varying logarithm barrier Lyapunov function (LBLF). Finally, the simulation results on CSTR are shown to reveal the validity of the developed control strategy.

A Fractional Order Proportional-Integral-Derivative Controller for Series Continuous Stirred Tank Reactor System

A Fractional Order Proportional-Integral-Derivative Controller for Series Continuous Stirred Tank Reactor System

Design of delayed PID‐type controllers for high‐order recycling systems with time delays using model reduction

AbstractIn even the simplest recycling system with a first‐order plus dead time model for the direct and recycling paths, the characteristic equation for the open‐loop transfer function contains an infinite number of poles. This is due to the presence of time delays in the internal dynamics. The task of designing a controller is therefore challenging from the perspective of control. This paper considers the problem of stabilising a high‐order delayed recycling system (with a possible unstable direct path) through the use of a reduced model in conjunction with a new PID (proportional‐integral‐derivative)‐type delayed controller. We state the condition under which the stability of the closed‐loop system is guaranteed and determine the sets of stabilising control parameters. The performance of our proposed strategy is illustrated using several numerical simulations of industrial processes, including an industrial quench column, two continuous stirred tank reactor systems with recycling and a reactor–separator process.

Robust Adaptive Generalized Predictive Control Based on Takagi-Sugeno Fuzzy Model

Predictive adaptive algorithms are powerful tools for controlling time varying systems. In particular, the Adaptive Generalized Predictive Control is widely used in different plants or processes. Nevertheless, a lack of robustness is often observed especially in case of large uncertainties in the model and in the presence of strong nonlinearities. Better solutions have been proposed using fuzzy modelling. In this paper, a direct adaptive version of the generalized predictive controller is developed. This allows an automatic online and real-time adjustment of local controller settings in order to maintain a proper level of performance. The contributions presented in this paper lie in the merging of local controllers using a fuzzy technique in order to produce a global controller using on-line parameters identification by a recursive lest squares type strategy. The stability of the closed-loop control system is demonstrated through the Lyapunov Stability Theory. Highly improved results are confirmed by simulation on an example of the commonly used Continuous Stirred Tank Reactor system.