ADAPTIVE ACTUAL PID CONTROL WITH AN ADJUSTABLE IDENTIFICATION INTERVAL

A method of design for an adaptive PID controller in an “actual” form is developed. The controller is expressed as an ideal PID controller with a low-pass filter. The parameters of the controller are determined by using the ITAE criterion based on a first-order plus dead time model which is directly obtained from an identified second-order model. In comparison with the ideal PID controller, the actual one gives better results especially for systems with noises.     

Generalized Common Model Control

Applying the Lie derivative concept, we propose a new control method of generalized common model control (GCMC) by generalizing the CMC algorithm to complex processes with the relative order larger than 1 to overcome the disadvantage of common model control (CMC), i.e., it can only apply to the processes with relative order of 1. The model of the non‐linear controlled plant is directly embedded in the controller, so that the controlled non‐linear system is a linear high order system in the no constrained control input, it is very easy to tune for the controller by applying the method of dominant poles. The simulation results show that the general common model controller is very effective for the non‐linear system.     

Superheated Steam Drying of a Single Particle in an Impinging Stream Dryer

Impinging stream contactors provide a novel configuration for drying and/or chemical reactions of particulates, pastes or suspensions which can be dispersed in a flowing gas stream. Essentially they consist of one or more highly turbulent “impingement” zones formed by collision of two opposing jets (OJ) in a confied channel or duct. The objective of this paper is to present computational fluid dynamic predictions for two-dimensional turbulent opposing jets over a range of nozzle-to-nozzle separations and jet Reynolds numbers for the simulation of single particle drying in these systems using superheated steam. A number of different turbulence models were tested ( e.g. high Reynolds, Lam-Bremhorst, Launder and Sharma models etc.). Predictions are performed in two distinct parts. In the first part a power law, finite volume method based on the “SIMPLEC” algorithm is used to solve the momentum and energy conservation equations for air in OJ systems in order to gain insight into their     

Predictive control of SCR denitrification system in thermal power plants based on GA-BP and PSO

Aiming at the characteristics of strong non-linearity and large inertia in the reaction process of a selective catalytic reduction (SCR) denitrification system, a predictive control algorithm based on a back propagation neural network optimized by genetic algorithm (GA-BP) and particle swarm optimization (PSO) is proposed. First, the prediction model of a SCR denitrification system is established by GA-BP. Second, output feedback and bias correction are used to reduce the prediction error. Third, the optimal inlet ammonia concentration is obtained by PSO. At the same time, in order to solve the problems of high dimension, large noise, and strong coupling in the original data of the SCR system in the process of establishing the prediction model, the least absolute shrinkage selection operator (LASSO) algorithm and the local outlier factor (LOF) detection algorithm are used to screen important variables and samples in the original data set of the SCR system to remove redundant variables and outliers. Finally, the simulation results show that the prediction model has good prediction accuracy and that the proposed predictive control method can achieve accurate control of ammonia injection concentration. This method improves the denitrification efficiency and reduces the NO_x emission concentration, which can provide good guidance for on-site production.     

COMPARISON OF EXPERIMENTAL AND MODELLING STUDIES FOR THE MICROWAVE DRYING OF IRONBARK TIMBER

Temperature profiles through boards during the microwave-assisted drying of Australian Ironbark timber have been investigated in this work in order to explore evidence for timber degrade via “charring” at internal temperatures below those required for pyrolysis (Brooke et al., 1998). A previously published model (Turner and Jolly, 1990a) describing one-dimensional microwave power absorption, based on the solution of Maxwell's equations, has been reviewed and significant limitations identified and overcome. Improvements included the use of a linear-mixing approach for the dielectric constants, the incorporation of temperature and moisture dependencies for these dielectric properties and the inclusion of diffusion within an overall system model. A control-volume technique has been used for predicting both moisture and temperature distributions within the timber, with a second-order finite-difference method being used to solve Maxwell's equations. The final model showed     

DYNAMIC BEHAVIOR OF REACTIVE DISTILLATION COLUMNS. EQUILIBRIUM SYSTEMS

This paper deals with the dynamic behavior of simultaneous reaction-separation systems which operate at or near the chemical equilibrium in the liquid phase. The process under study comprises a whole set of “instantaneous”, and very fast reversible reactions where the difference in volatilities favors both the progress of reaction and product separation. The main aim of our study is to gain a deeper understanding of the dynamic behavior of distillation columns by using a model that is simple and efficient, yet informative. This kind of model is outstanding for synthesis and design of control schemes which require a careful modelling and understanding of process response to different changes in the environment. We use a suitable transformation of variables (after Barbosa and Doherty, 1988b) in order to define a new set of state variables; as a result, the balance equations become identical to those for conventional distillation. Also, an efficient physicochemical algorithm that can handle both the original and new state variables is used. Thus, a composition-holdup dynamic model is simulated in the “transformed field” using a stage-by-stage approach. To further reduce computational time, the transformed problem has also been solved by means of a reduction procedure based on approximating by orthogonal polynomials the transformed composition and flow profiles in the column. The performance of the two proposed methods are compared by using the top section of a quaternary reactive column. The results obtained shown that reactive distillation dynamics has certain peculiarities derived from superimposing reaction and separation phenomena.     

Capacity intermittent titration technique (CITT): A novel technique for determination of Li solid diffusion coefficient of LiMn2O4

The capacity intermittent titration technique (CITT) has been developed on basis of the ratio of the potentio-charge capacity to the galvano-charge capacity (RPG) method to determine continuously the solid diffusion coefficient D of the intercalary species within insertion-host materials. In experiment, CITT is based on the capacity response of galvano–potentio-charge in a small voltage region. In theory, CITT is based on the linear equations of D versus q (value of RPG) in different range of q. By the CITT, the Li⁺ solid diffusion coefficients within LiMn₂O₄ have been determined at different voltages and different galvano-charge currents. Results shows that the order of magnitude of D varies non-linearly with the “W” shape from 10⁻⁹ to 10⁻¹¹ cm² s⁻¹ in the voltage range from 3.3 to 4.3 V. The galvano-charge current also leads to the error due to the semi-conductive character of LiMn₂O₄, and the maximal error may reach as much as one order of magnitude. In addition, the main approximations that lead to errors of CITT are qualitatively analyzed.     

UNBIASED IDENTIFICATION OF CONTINUOUS-TIME PARAMETRIC MODELS USING A TIME-WEIGHTED INTEGRAL TRANSFORM

A time-weighted integral transform is presented to identify a continuous SISO or MIMO parametric model based on a single dynamic test under open-loop or closed-loop operation. Moving-horizon algorithms are proposed to obtain unbiased estimates of the model parameters. The off-line algorithm in a least-squares form and the on-line algorithm in a recursive form are provided. An effective technique based on pattern recognition is also developed to determine the system order and time delay from observed data in a simple manner. Furthermore, the proposed method can be easily applied as a model reduction technique that results in an ideal model with delay for any specified order.     

MODELING AND CONTROL OF A TEMPERATURE-BASED HIGH-PURITY DISTILLATION COLUMN*

Industrial distillation columns typically lack adequate on-line instrumentation required for implementing high performance, model-based control systems which depend on composition measurements. The typical practice is to base product quality control schemes instead on tray temperature measurements since these are usually available on-line. While this strategy has been applied with success on a wide variety of distillation columns, some special considerations may be necessary in dealing with high-purity columns. This paper presents results which address some of the key issues involved with the use of tray temperature measurements as surrogates for composition measurements, and with the development of appropriate input/output models for multi variable control of high-purity columns. For the purpose of illustration, we investigate the performance of several model predictive control schemes based on a linear model, a “high frequency” model, and a nonlinear temperature transformation, along with that of conventional multiloop PID control. Closed-loop control performances are compared for setpoint changes as well as for changes in unmeasurable load disturbances.     

Unbiased identification of continuous-time parametric models using a time-weighted integral transform

A time-weighted integral transform is presented to identify a continuous SISO or MIMO parametric model based on a single dynamic test under open-loop or closed-loop operation. Moving-horizon algorithms are proposed to obtain unbiased estimates of the model parameters. The off-line algorithm in a least-squares form and the on-line algorithm in a recursive form are provided. An effective technique based on pattern recognition is also developed to determine the system order and time delay from observed data in a simple manner. Furthermore, the proposed method can be easily applied as a model reduction technique that results in an ideal model with delay for any specified order.     

Two phase mathematical model for a bio-trickling reactor for the production of ultra low sulfur diesel (ULSD) from deeply hydrodesulfurized diesel

A trickle bed reactor (TBR) having a diameter of 0.066 m and a height of 0.6 m has been used for the bio-desulfurization of hydrotreated diesel fraction having sulfur concentration in the range of 200–540 ppm. Rhodococcus sp. (NCIM 2891, Pune) has been used to degrade the residual organo-sulfur compounds present in deeply hydrodesulfurized diesel. The microorganisms have been immobilized on the packing material prior to desulfurization within the trickle bed reactor. The volumetric flow rate and hence, the substrate loading rate have been used as the parameters. Sulfur reduction within the range of 84–95% has been achieved. To avoid the excess accumulation of the biomass within the reactor, backwashing technique is incorporated. For such desulfurization, batch studies have been conducted in Erlenmeyer flasks maintaining the concentration of diesel in the range of 0–100% in a diesel supplemented sulfur-free aqueous medium. The concentration of biomass with time has been monitored using dry cell weight method. The concentration of sulfur has been determined by “trace sulfur in petroleum distillate by nickel reduction” (UOP 357-80) method. From the growth curve, it is observed that the system follows uninhibited Monod type model within the range of substrate studied. A systematic and programmed investigation has been carried out to determine the growth kinetic parameters, namely maximum specific growth rate, saturation constant K_s and yield coefficient Y_X/S. A deterministic mathematical model for the TBR has been developed using judicious assumptions to predict its performance characteristics.     

Development of activity coefficient model based on COSMO method for prediction of solubilities of solid solutes in supercritical carbon dioxide

A COSMO base activity coefficient model was newly developed to predict the solubilities of solid solutes in supercritical carbon dioxide. This activity coefficient model describes that the system is composed of the surface segments on the solvent molecule and vacancy unlike the conventional model based on COSMO method. The density change of supercritical fluid can be represented by the change of the surface area of the vacancy. This prediction model is referred to “COSMO-vac (vacancy)” model. The solubilities of 16 pharmaceuticals in supercritical carbon dioxide were predicted by COSMO-vac model. The averaged deviations between the logarithmic experimental and predicted results are smaller than unity. Furthermore, the predicted results for the solutes composed of only C, H and O atoms are better than those for the solutes including the other atoms. The percentage of the predicted results within the order of the experimental data at the pressure over 15 MPa is higher than that at the pressures below 15 MPa.     

使用博弈差分算法的电站锅炉高效低污染燃烧均衡优化

提高电站锅炉热效率,降低NO_x等污染物的排放量是电站节能减排必须解决的问题。经过采用经量子遗传算法）QGA）优化参数后的最小二乘支持向量机（LSSVM-QGA）建立燃烧优化模型,预测的锅炉热效率和NO_x排放量的平均相对误差分别达到了0.054%和1.229%,其预测精度及泛化能力均较优,有更强的适用性能。在此模型基础上,提出一种采用自适应缩放因子与交叉因子和共享函数机制的差分进化算法（DE）,通过其演化博弈论中的NASH均衡,实现锅炉燃烧的多目标优化,结果表明,基于NASH均衡的优化方法可以得到操作变量的最优解集,能够更好地改善运行工况,最终可以实现削峰填谷,使电站锅炉保持一个稳定均衡的燃烧状态。     

Two-degree-of-freedom output feedback controllers for nonlinear processes

In this work, a nonlinear output feedback control algorithm is proposed, in the spirit of model-state feedback control. The structure provides state estimates using a process model, the measured output, and the residual between the model output and the measured output. These estimates will track the process states at a rate determined by a set of tunable parameters. An algebraic transformation of the state estimates is incorporated in the control structure to ensure that the input/output gain of the observer matches the model upon which the static state feedback control law is based. The transformed states are then used in the control law. This leads to a controller of minimal order possessing integral action. The control structure is shown to have the same properties as the standard model-state feedback structure. The resulting algorithm is a two-degree of freedom control law, in the sense that the control action is not a function of the error only, but the output and the set point are processed in different ways. Finally, a simulation example using an exothermic CSTR operating at an open-loop unstable steady state is used to demonstrate the closed-loop performance of the proposed method.     

Identification of the reaction zones occurring in a commercial-scale Sasol–Lurgi FBDB gasifier

Gasification behaviour is particle dependent, whilst gasifier (reactor) behaviour is an averaging process of individual responses of each particle. It was hypothesized, that if it were possible to extract and analyze particles from different reaction zones within a gasifier, it may be likely to enhance the understanding of the contribution that these particles make towards gasification. This better understanding of the particle-type compositional responses could act as an enabler to further manipulate and improve gasifier performance.The primary focus of this study was to evaluate a sequential (axial) sampling “turn-out” methodology of a quenched fixed-bed commercial-scale Sasol–Lurgi gasifier, in order to present samples that accurately describe operational aspects occurring in the reaction zones within the reactor. Characterization of the chemical properties of the sample increments were expected to deliver distinct profiles of the drying, pyrolysis, reduction and combustion (ash-bed) zones, which could be used to advance the kinetic modeling capability of the process. In order to interpret the coal property transformational behaviour occurring within the commercial-scale gasifier, the proximate, Fischer tar, ultimate, and coal char CO₂ reactivity analysis were conducted.The pyrolysis zone was found to be the largest reaction zone situated below the drying zone within the gasifier, followed by the reduction zone, and combustion (ash-bed) zones. Whilst the boundaries of the pyrolysis zone were very clearly defined by the residual volatile matter distribution profile, distinctive regional overlap with a “slow pyrolysis with gasification” region was observed in the bottom half of the pyrolysis zone, above which a “rapid de-volatilization” region existed. The reduction zone was found to also exhibit an overlap in zonal fronts, i.e. a gasification region occurred below the pyrolysis zone and co-existed in equal proportions, with an oxidation frontal region occurring above the combustion zone. The combustion zone was found to be very shallow, below which the ash-bed region existed.The findings clearly suggest that text book pictures showing axially-depicted reaction zones occurring within the fixed-bed gasifier, i.e. drying, pyrolysis, gasification and combustion, inadequately describe the “real” situation and in practice, overlap of reaction regions within zones indeed also transpire.     

A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory

Corn to sugar process has long faced the risks of high energy consumption and thin profits. However, it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes. Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions. In this paper, a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes, which contains data preprocessing, dimensionality reduction, multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method. In the established model, dextrose equivalent value is selected as the output, and 654 sites from the DCS system are selected as the inputs. LASSO analysis is first applied to reduce the data dimension to 155, then the inputs are dimensionalized to 50 by means of genetic algorithm optimization. Ultimately, variable importance analysis is carried out by the extended weight connection method, and 20 of the most important sites are selected for each neural network. The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%, which have a better prediction result than other models, and the 20 most important sites selected have better explicable performance. The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.     

GENERIC MODEL ADAPTIVE CONTROL

Generic Model Control (GMC) is a process model based control algorithm incorporating a process model directly within the control structure. It has been shown to produce excellent control, despite reasonable modelling errors. In this paper an algorithm is developed within a GMC framework which reduces the effect of larger modelling errors by regularly updating the model parameters. This new adaptive algorithm is capable of adapting model parameters in a nonlinear model, where the parameters appear in a nonlinear manner. Several examples are presented to illustrate the principles of the technique.     

暖通空调系统基于SMITH预估自校正控制算法

目前暖通空调控制系统中普遍存在运行效率低下、能耗浪费严重的现象,主要原因在于暖通系统本身具有时变、时滞等非线性特性,使得传统的控制方法无法取得良好的控制性能。如何克服暖通空调系统时变和时滞特性所带来的不利影响,是提高暖通空调系统控制回路性能的重要途径之一。本文设计了一种基于SMITH预估的自校正控制算法,首先通过在线辨识包含时滞参数在内的暖通空调对象模型,进而采用SMITH预估器对时滞进行补偿,而SMITH预估器中的PI控制器则用于提高鲁棒性,并实现控制器参数的实时更新。仿真结果表明该算法性能要优于传统的控制方法。     

近红外光谱技术在中草药口服液在线质量监控中的模型建立和模型转移

应用近红外光谱技术在线监测工业产品质量时,会出现环境条件变化或仪器的部件如探头或光纤更换的情况,使原模型不再具有原来的预测效果,但是完全从头开始采集数据重新建立新模型工作量大,造成原来宝贵的模型和数据的浪费。为了解决这一矛盾,本文以一种中草药口服液中多糖含量、可溶性固形物含量及pH 为研究对象,利用近红外光谱技术对其进行实时在线检测,研究了主从机分辨率不同的光谱之间的模型转移。模型转移过程利用已建模完成并成功上线应用的模型为原模型,在不能获取原主仪器和从仪器一一对应的标准标样的条件下,找到虚拟标样建立转移矩阵。以直接标准化法结合主成分分析降维作为模型转移方法,以质量指标化学参考值与预测值间的相对误差为指标筛选最佳模型。模型转移结果显示,多糖模型预测值与化学参考值间的相对误差可控制在10%以内,可溶性固形物相对误差在5%以内,pH 相对误差在3%以内。在线生产使用表明,转移的模型同原模型一样可有效应用于在线、快速对质量指标做出准确的预测。结果表明,本文提出的采用虚拟标样的模型转移方法对于无法获得主从机一一对应的标准标样的情况下的模型转移,是一个可行的有效方法。