Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

Stirred reactors are key equipment in production,and unpredictable failures will result in significant economic losses and safety issues.Therefore,it is necessary to monitor its health state.To achieve this goal,in this study,five states of the stirred reactor were firstly preset:normal,shaft bending,blade eccentricity,bearing wear,and bolt looseness.Vibration signals along x,y and z axes were collected and analyzed in both the time domain and frequency domain.Secondly,93 statistical features were extracted and evaluated by ReliefF,Maximal Information Coefficient(MIC)and XGBoost.The above evaluation results were then fused by D-S evidence theory to extract the final 16 features that are most relevant to the state of the stirred reactor.Finally,the CatBoost algorithm was introduced to establish the stirred reactor health monitoring model.The validation results showed that the model achieves 100%accuracy in detecting the fault/normal state of the stirred reactor and 98%accuracy in diagnosing the type of fault.

Suspension quality and power consumption of the complex multiphase flow field in a draft-tube stirred reactor

Experiment and simulation were used to study the flow fields of a liquid-solid-solid three-phase system in a draft-tube stirred reactor with a six-flat-bladed turbine impeller(BT)and a 45°pitched six-bladed upflow turbine impeller(PBTU45).The computational fluid dynamics(CFD)formulation was based on a 3D Eulerian multi-fluid model along with a renormalization group(RNG)k-ε turbulence model,which took into account the inter-phase momentum exchange between different phases.The CFD model was validated using experimental data,and the suspension quality was validated by determining the standard deviation of the solid concentration.Good solid suspension was achieved in flow fields agitated by the BT impeller at low impeller speeds.The draft tube adversely affected solid dispersion at most impeller speeds.In flow fields agitated by the PBTU45 impeller,good solid suspension required a faster impeller speed.The suspension quality with the draft tube was better than that without the draft tube under most operating conditions.The power consumption of the BT or PBTU45 impeller with the draft tube was less than that without the draft tube at most impeller speeds.

Modeling and Control of a Continuous Stirred Tank Reactor Based on a Mixed Logical Dynamical Model

A novel control strategy for a continuous stirred tank reactor(CSTR)system,which has the typical characteristic of strongly pronounced nonlinearity,multiple operating points,and a wide operating range,is initiated from the point of hybrid systems.The proposed scheme makes full use of the modeling power of mixed logical dy- namical(MLD)systems to describe the highly nonlinear dynamics and multiple operating points in a unified framework as a hybrid system,and takes advantage of the good control quality of model predictive control(MPC) to design a controller.Thus,this approach avoids oscillation during switching between sub-systems,helps to relieve shaking in transition,and augments the stability robustness of the whole system,and finally achieves optimal(i.e. fast and smooth)transition between operating points.The simulation results demonstrate that the presented ap- proach has a satisfactory performance.

Development of Linearizing Feedback Control with a Variable Structure Observer for Continuous Stirred Tank Reactors

This paper deals with the design of an observer-based nonlinear control for continuous stirred tank reactors(CSTR).A variable structure observer is constructed to estimate the whole process state variables.This observer is basically the conventional Luenberger observer with an additional switching term used to guarantee the robustness against modeling errors.The observer is coupled with a nonlinear controller,designed based on input-output linearization for controlling the reactor temperature.The asymptotical stability of the closed-loop system is shown by the Lyapunov stability theorem.Finally,computer simulations are developed for showing the performance of the proposed approach.

Model identification of continuous stirred tank reactor based on QKLMS algorithm

The continuous stirred tank reactor(CSTR)is one of the typical chemical processes.Aiming at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is proposed.The QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification,which can compress the input or feature space and suppress the growth of the radial basis function(RBF)structure in the kernel learning algorithm.To verify the effectiveness of the algorithm,it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product concentration.In additiion,the proposed algorithm is further compared with least squares support vector machine(LS-SVM),echo state network(ESN),extreme learning machine with kernels(KELM),etc.The experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.

Gas dispersion and solid suspension in a three-phase stirred tank with triple impellers

The hydrodynamics is still not fully understood in the three-phase stirred tank equipped with multi-impeller due to the intensive interaction between phases.In this work,the solid critical suspension speed(NJSG),relative power demand(RPD)and overall gas holdup(ε_G)were measured in an air–water–glass beads stirred tank equipped with multi-impeller,which consists of a parabolic blade disk turbine below two down-pumping hydrofoils.Results show that either the NJSGor the specific power consumption increases when increasing the volumetric solid concentration or superficial gas velocity.RPD changes less than 10%when solid volumetric concentration ranges from 0 to 15%.ε_G decreases with the increase of solid concentration,and increases with the increase of both superficial gas velocity and the total specific power consumption.The quantitative correlations of NJSG,RPD andεGwere regressed as the function of superficial gas velocity,specific power consumption,Froude number and gas flow number,in order to provide the reference in the design of such three-phase stirred tank with similar multi-impellers.

Bioleaching of chalcopyrite by mixed culture of moderately thermophilic microorganisms

A mixed culture of moderately thermophilic microorganisms was enriched from acid mine drainages(AMDs)samples collected from several sulphide mines in China,and the bioleaching of chalcopyrite was conducted both in shake flask and bioreactor.The results show that in the shake flask,the mixture can tolerate 50 g/L chalcopyrite after being acclimated to gradually increased concentrations of chalcopyrite.The copper extraction increases obviously in bioleaching of chalcopyrite with moderately thermophilic microorganisms supplemented with 0.4 g/L yeast extract at 180 r/min,74% copper can be extracted in the pulp of 50 g/L chalcopyrite after 20 d.Compared with copper extractions of mesophilic culture,unacclimated culture and acclimated culture without addition of yeast extract,that of accliniated culture with addition of yeast extract is increased by 53%,44% and 16%,respectively.In a completely stirred tank reactor,the mass fraction of copper and total iron extraction reach up to 81% and 56%,respectively.The results also indicate that it is necessary to add a large amount of acid to the pulp to extract copper from chalcopyrite effectively.

Determination of Heroin Based on Analyte Pulse Perturbation to an Oscillating Chemical Reaction

A new analytical method is proposed for the determination of heroin based on a sequential perturbation caused by trace amounts of heroin in the Cu(Ⅱ)-catalyzed oscillating reaction between hydrogen peroxide and sodium thiocyanate in an alkaline medium with the aid of a continuous-flow stirred tank reactor(CSTR). The method relies on the linear relationship between the change in oscillation period of the system and the concentration of heroin, with a detecting limit of 4.0×10^(-7) mol/L. The calibration curve fits a linear equation very well when the concentration of heroin is in the range of 2.0×10^(-6)_1.2×10^(-5) mol/L(r=0.9971). This method features good precision(RSD=0.98%). The influences of temperature, injection point, flow rate and reaction variables on the oscillation period were investigated in detail and a possible mechanism of the performance of heroin in the Cu(Ⅱ)-catalyzed oscillating reaction system is also discussed. The proposed method opens a new avenue for the determination of heroin.

PSO Based Multi-Objective Approach for Controlling PID Controller

CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities in its control and design.Dynamic performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for CSTR.The reactor temperature changes in either direction from the defined reference value.It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass quality.Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential.In this paper,a conventional Proportional Integral Derivative(PID)controller is designed.The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters.Hence,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation.In the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response.In this article,a multi-objective function is proposed for PSO based controller design of CSTR.

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.

Reactive Flash Simulation of the Continuous Melt Transesterification Process of Polycarbonate

A model of the continuous melt transesterification process of bisphenol-A and diphenyl carbonate in a continuous stirred tank reactor to produce polycarbonate is presented. The model is developed by using the molecular species model of polycarbonate melt polycondensation and the modeling method of reactive flash. Liquid phase is treated as perfect mixed flow and the vapor phase is assumed following the ideal gas law. With this model, the continuous melt transesterification process of bisphenol-A and diphenyl carbonate is examined with respect to different orocess parameters.

Identification of LPV Models with Non-uniformly Spaced Operating Points by Using Asymmetric Gaussian Weights

In this paper, asymmetric Gaussian weighting functions are introduced for the identification of linear parameter varying systems by utilizing an input-output multi-model structure. It is not required to select operating points with uniform spacing and more flexibility is achieved. To verify the effectiveness of the proposed approach, several weighting functions, including linear, Gaussian and asymmetric Gaussian weighting functions, are evaluated and compared. It is demonstrated through simulations with a continuous stirred tank reactor model that the oroposed aonroach nrovides more satisfactory aonroximation.

Stabilization of CSTR w ith Self-tuning Sliding Mode Controller Using T-S Fuzzy Linearization

A self-tuning reaching law based sliding mode control(SMC)theory is proposed to stabilize the nonlinear continuous stirred tank reactor(CSTR).T-S fuzzy logic is used to build a global fuzzy state-space linear model.Combing the traits of SMC and CSTR,three fuzzy rules can meet the requirements of controlled system.The self-tuning switch control law which can drive the state variables to the sliding surface as soon as possible is designed to ensure the robustness of uncertain fuzzy system.Lyapunov equation is applied to proving the stability of the sliding surface.The simulations show that the proposed approach can achieve desired performance with less chattering problem.

Experimental Study on Hydrogenation of SiCl_4 to SiHCl_3 in a Stirred Bed Reactor

The hydrogenation of SiCl_4 to SiHCl_3 was studied in a stirred bed reactor with CuCl catalyst.The properties of the CuCl catalysts and silicon particles before and after the reaction were characterized by SEM,XRD and XPS.The XRD showed that the active component of Cu3Si was formed during the reaction,and the EDX proved the molar ratio of Cu and Si on the region of apertures.The valent of Cu was discussed by XPS before and after the hydrogen reaction.Then the effects of the reaction temperature,pressure,molar ratio of H2 to SiC l4,weight hourly space velocity(WHSV),and catalyst loading were studied.The results showed that the conversion rate of Si Cl4 was about 38%at WHSV of 190 Nm3/(t·h),temperature of 540℃,pressure of 1.8 MPa,catalyst loading of 0.9%(ω),and molar ratio of H2 to Si Cl4 1.7:1.Based on the experemental results,a reaction mechanism was proposed,which involved the continuous consumption of silicon(many apertures was showed on SEM image)and formation of new Cu3Si active component during the hydrogenation reaction.

Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation processes.BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors.When the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring.Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant analysis.In addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.

Nonlinear model predictive control based on hyper chaotic diagonal recurrent neural network

Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.

Output neighboring-extremal and NCO tracking control in the optimal operation of CSTR

A new optimizing framework of process operation is proposed to deal with optimizing op- eration of continuous stirred tank reactor （CSTR）. The optimization framework includes two layers： the first layer, necessary condition of optimally （NCO） tracking controller, calculates the optimal set-point of the process; and the second layer, output neighboring-extremal controller, calculates the input values of the controlled plant. The algorithm design and convergent analysis of output neighboring-extremal controller are discussed emphatically, and in the case of existing parametric uncertainty, the approach is shown to converge to the optimum atmost in two iterations. At last the approach is illustrated by simulation results for a dynamic CSTR.

Nonlinear model predictive control with guaranteed stability based on pseudolinear neural networks

A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor.It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.

Comprehensive Assessment of STGSA Generated Skeletal Mechanism for the Application in Flame-Wall Interaction and Flame-Flow Interaction

In this study,we conduct a thorough evaluation of the STGSA-generated skeletal mechanism for C_(2)H_4/air.Two STGSA-reduced mechanisms are taken into account,incorporating basic combustion models such as the homogeneous reactor model,one-dimensional flat premixed flame,and non-premixed counterflow flame.Subsequently,these models are applied to more complex combustion systems,considering factors like flame-flow interaction and flame-wall interaction.These considerations take into account additional physical parameters and processes such as mixing frequency and quenching.The results indicate that the skeletal mechanism adeptly captures the behavior of these complex combustion systems.However,it is suggested to incorporate strain rate considerations in generating the skeletal mechanism,especially when the combustion system operates under high turbulent intensity.

A Chemical Reactor Network for Oxides of Nitrogen Emission Prediction in Gas Turbine Combustor

This study presents the use of a new chemical reactor network(CRN) model and non-uniform injectors to predict the NOx emission pollutant in gas turbine combustor. The CRN uses information from Computational Fluid Dynamics(CFD) combustion analysis with two injectors of CH4-air mixture. The injectors of CH4-air mixture have different lean equivalence ratio, and they control fuel flow to stabilize combustion and adjust combustor's equivalence ratio. Non-uniform injector is applied to improve the burning process of the turbine combustor. The results of the new CRN for NOx prediction in the gas turbine combustor show very good agreement with the experimental data from Korea Electric Power Research Institute.