基于格子玻尔兹曼法的连续式搅拌反应釜流场数值模拟

为探究高含固厨余垃圾条件下连续式搅拌反应釜(continuousstirred-tankreactor,CSTR)的流场特性,课题组采用一种新颖的计算流体动力学方法格子玻尔兹曼法(latticeBoltzmannmethod,LBM)进行研究。模拟了3种桨型的流场特性,并通过对比不同转速下的扭矩实验值与模拟值,评估了该方法的可靠性,结果表明在低转速时实验值与模拟值的相对偏差均小于5%;随后考察了不同搅拌桨的速度分布、扭矩以及死区占比的变化情况。模拟结果显示:在处理含固体积分数为20%的厨余垃圾时,螺带式搅拌桨几乎带动整个反应釜内物料运动,并且其死区占比相对较小;使用螺带式搅拌桨时,随着转速的增加,反应釜中死区占比会有所下降。因此,综合考虑反应釜内的速度分布、扭矩及死区占比,用于搅拌高含固的厨余垃圾的连续式搅拌反应釜的最佳搅拌条件是转速为40r/min的螺带式搅拌桨。课题组所提出的格子玻尔兹曼法方法可用于模拟低转速条件下高含固厨余垃圾的流动特性。

Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

Adaptive Local Outlier Probability for Dynamic Process Monitoring

Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

Modeling and Simulation of Ethylene Polymerization in Industrial Slurry Reactor Series

A five-site comprehensive mathematical model was developed to simulate the steady-state behavior of industrial slurry polymerization of ethylene in multistage continuous stirred tank reactors. More specifically, the effects of various operating conditions (i.e., inflow rates of catalyst, hydrogen and comonomer) on the molecular structure and properties of polyethylene (i.e.,Mw,Mn, polydispersity index (IPD), melt index, density, etc.) are fully assessed. It is shown that the proposed comprehensive model is capable of simulating the steady-state operation of an industrial slurry stirred tank reactor series. It is demonstrated that changing the catalyst flow rate, changes simultaneously the mean residence-time in both reactors, which plays a significant role on the establishment of polyethylene architecture properties such as molecular mass and IPD. The melt index and density of polyethylene are mainly controlled by hydrogen and comonomer concentration, respectively.

A local and global statistics pattern analysis method and its application to process fault identification

Traditional principal component analysis （PCA） is a second-order method and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis （SPA） framework has been incorporated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis （LGSPA） method, which integrates SPA framework and locality pre- serving projections within the PCK is proposed to utilize various statistics and preserve both local and global in- formation in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution （IRBC） plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables＇ statistics, a new contribution of process variables is built to locate fault variables. The simula- tion results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.

Temperature fault-tolerant control system of CSTR with coil and jacket heat exchanger based on dual control and fault diagnosis

For the characteristics of the continuous stirred-tank reactor(CSTR) with coil and jacket cooling system,a CSTR temperature dual control solution based on the analysis of the CSTR exothermic reaction control characteristic was proposed for an organic material polymerization production.The control solution has passive fault-tolerant ability for the jacket cooling water cutting off fault and active fault-tolerant potential for the coil cooling water cutting off fault,and it has good control ability,high saving energy and reducing consumption performance.Fault detection and diagnosis and fault-tolerant control strategy are designed for the coil cooling fault to achieve the active fault-tolerant control function.The CSTR temperature dual control,process fault detection and diagnosis and active fault-tolerant control were full integrated into the CSTR temperature fault-tolerant control system,which achieve fault tolerance control of CSTR temperature for any severe malfunction of jacket cooling or coil cooling cutting off,and the security for CSTR exothermic reaction is improved.Finally,the effectiveness of this system was validated by semi-physical simulation experiment.

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.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

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.

A self-tuning control method for Wiener nonlinear systems and its application to process control problems

Many chemical processes can be modeled as Wiener models, which consist of a linear dynamic subsystem followed by a static nonlinear block. In this paper, an effective discrete-time adaptive control method is proposed for Wiener nonlinear systems with uncertainties. The parameterization model is derived based on the inverse of the nonlinear function block. The adaptive control method is motivated by self-tuning control and is derived from a modified Clarke criterion function, which considers both tracking properties and control efforts. The uncertain parameters are updated by a recursive least squares algorithm and the control law exhibits an explicit form. The closed-loop system stability properties are discussed. To demonstrate the effectiveness of the obtained results, two groups of simulation examples including an application to composition control in a continuously stirred tank reactor(CSTR) system are studied.

Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.

Using a Membrane Reactor for Sucrose Hydrolysis： The Effect of Reactor＇s Design on Invertase Stability

Invertase hydrolyses sucrose, produces inverted sugar syrup, which is used, mainly, as a food composition in industries. To carry out the hydrolysis properly, the invertase should be stable in the soluble form through a considerable reaction period and recovered afterwards. The chosen reactor was a CSTR-type （continuous stirred tank reactor-type） coupled with an UFM （ultrafiltration-membrane）, the so-called MR （membrane reactor）. The varied parameters were： sucrose concentration （10-300 mM）, temperature （5-65 ℃）, reaction volume （14 mL and 65 mL）, stirring （100-500 rpm）, volumetric feeding rate （2.2-12 mL/h） and UFM MWCO （molecular weight cut off） （10, 20, 30, 50 and 100 kDa）. The invertase kinetic constants （KM = 23.5 mM; Vmax = 2,758 μmolgluJmin-mgprot; Ea = 9.1 kcal/mol） and the temperature deactivation energy （Ead= 20 kcal/mol） were calculated. Moreover, the invertase was unstable as the MR capacity diminished and the agitation increased up to 500 rpm most likely due to the damaging effect of shearing forces （present inside the MR） on the invertase molecule. Finally, both the MWCO and the chemical nature of the UFM affected the invertase stability along the hydrolysis. The enzyme stability increased as the UFM cut-off decreased, the highest value being observed with the 10 kDa-UFM.