Influence of aggregates on chloride diffusion coefficient into mortar

In order to determine the influence of aggregates on chloride ion ingress, mortar specimens were cast with different aggregate size distribution and the same aggregate volume content. One side of the specimen was in contact with alkaline solution containing sodium chloride. The process of chloride ingress is due to diffusion. The effect of the interfacial transition zone (ITZ) around aggregate and of the tortuosity due to aggregates have been taken into account for interpreting the experimental data obtained. These two competing effects have been quantified by using the theory of composite materials. It appears that the diffusion coefficient varies as a function of the ITZ volume content and of the tortuosity.     

溶胶-凝胶法制备多孔陶瓷的研究进展

本文综合论述了溶胶-凝胶法制备多孔陶瓷的工艺研究进展以及影响溶胶-凝胶法制备多孔陶瓷的主要因素，并讨论了今后溶胶-凝胶法制备多孔陶瓷的主要研究问题及其发展前景。     

环烷酸钴的制备

论述了以金属钴和环烷酸为原料合成环烷酸钴的工艺，考察了氯化钴合成工艺和处理工艺，氯化钴浓度，环烷酸酸值，碱浓度对环烷酸钴钴含量的影响，合成的氯化钴经处理达到国家标准要求，合成的氯化钴不经处理可直接用于合成环烷酸钴，产品环烷酸钴达到技术指标要求。     

L2 disturbance attenuation for highly dissipative nonlinear spatially distributed processes via HJI approach

For many practical industrial spatially distributed processes (SDPs), their dynamics are usually described by highly dissipative nonlinear partial differential equations (PDEs). In this paper, we address the L₂ disturbance attenuation problem of nonlinear SDPs using the Hamilton–Jacobi–Isaacs (HJI) approach. Firstly, by collecting an ensemble of PDE states, Karhunen–Loève decomposition (KLD) is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. Subsequently, these EEFs together with singular perturbation (SP) technique are used to obtain a finite-dimensional slow subsystem of ordinary differential equation (ODE) that accurately describes the dominant dynamics of the PDE system. Secondly, based on the slow subsystem, the L₂ disturbance attenuation problem is reformulated and a finite-dimensional H_∞ controller is synthesized in terms of the HJI equation. Moreover, the stability and L₂-gain performance of the closed-loop PDE system are analyzed. Thirdly, since the HJI equation is a nonlinear PDE that has proven to be impossible to solve analytically, we combine the method of weighted residuals (MWR) and simultaneous policy update algorithm (SPUA) to obtain its approximate solution. Finally, the simulation studies are conducted on a nonlinear diffusion-reaction process and a temperature cooling fin of high-speed aerospace vehicle, and the achieved results demonstrate the effectiveness of the developed control method.     

Simultaneous closed-loop tuning of cascade controllers based directly on set-point step-response data

This study presents a novel closed-loop tuning method for cascade control systems, in which both primary and secondary controllers are tuned simultaneously by directly using set-point step-response data without resorting to process models. The tuning method can be applied on-line to improve the performance of existing underperforming cascade controllers by retuning controller parameters, using routine operating data. The goal of the proposed design is to obtain the parameters of two proportional-integral-derivative (PID)-type controllers, so that the resulting inner and outer loops behave as similarly as possible to the appropriately specified reference models. The tuning rule and optimization problem related to the proposed design are derived. Based on the rationale behind cascade control, the secondary controller is designed based on disturbance rejection to quickly attenuate disturbances. The primary controller is designed to accurately account for the inner-loop dynamics, without requiring an additional test. In addition, robustness considerations are included in the proposed tuning method, which enable the designer to explicitly address the trade-off between performance and robustness for inner and outer loops independently. Simulation examples show that the proposed method exhibits superior control performance compared with the previous (model-based) tuning methods, confirming the effectiveness of this novel tuning method for cascade control systems.     

A tutorial review of economic model predictive control methods

An overview of the recent results on economic model predictive control (EMPC) is presented and discussed addressing both closed-loop stability and performance for nonlinear systems. A chemical process example is used to provide a demonstration of a few of the various approaches. The paper concludes with a brief discussion of the current status of EMPC and future research directions to promote and stimulate further research potential in this area.     

Feedback PID-like fuzzy controller for pH regulatory control near the equivalence point

In this research the use of a feedback PID-like fuzzy controller scheme for pH control is presented to deal with instability problems near the equivalence point in neutralization processes. State space analysis of the titration curves and a fuzzy clustering algorithm based on calculating a measure of potential derived from the square distance of the pH data are complementary applied to define the membership structure and the fuzzy sets of the controller. To test the performance of the controller, both simulated and experimental runs were used. The fuzzy controller was tested for compensating step-change perturbations of propionic acidic flow rates, propionic acid concentration, and buffering conditions. Stationary cycling behavior has been observed for large loads of acidic flow rates. It was found that though the rejection time was strongly dependent on the mean residence time of the liquid solutions, the proposed controller keep the neutralization process operating close to the specified set point of pH = 7.     

Smart grid coordination in building HVAC systems: EMPC and the impact of forecasting

Energy consumption by heating, ventilation, and air conditioning (HVAC) systems exhibits a clear correlation with electricity prices. The method of economic model predictive control (EMPC) can be used in conjunction with thermal energy storage (TES) to time-shift power consumption away from periods of high demand to periods of low energy cost. Dynamic electricity pricing and weather condition forecasts can be readily incorporated within this methodology. Unfortunately, the receding horizon nature of this control strategy makes it very susceptible to the quality of the forecasts used. To this end, the development and implementation of several forecasting methods will be discussed. Finally, the EMPC performance of these methods will be assessed on a simple building example using active TES.     

A model structure-driven hierarchical decentralized stabilizing control structure for process networks

Based on the structure of process models a hierarchically structured state-space model has been proposed for process networks with controlled mass convection and constant physico-chemical properties. Using the theory of cascade-connected nonlinear systems and the properties of Metzler and Hurwitz matrices it is shown that process systems with controlled mass convection and without sources or with stabilizing linear source terms are globally asymptotically stable. The hierarchically structured model gives rise to a distributed controller structure that is in agreement with the traditional hierarchical process control system structure where local controllers are used for mass inventory control and coordinating controllers are used for optimizing the system dynamics. The proposed distributed controller is illustrated on a simple non-isotherm jacketed chemical reactor.     

Fault identification for process monitoring using kernel principal component analysis

In this research, we develop a new fault identification method for kernel principal component analysis (kernel PCA). Although it has been proved that kernel PCA is superior to linear PCA for fault detection, the fault identification method theoretically derived from the kernel PCA has not been found anywhere. Using the gradient of kernel function, we define two new statistics which represent the contribution of each variable to the monitoring statistics, Hotelling's T²and squared prediction error (SPE) of kernel PCA, respectively. The proposed statistics which have similar concept to contributions in linear PCA are directly derived from the mathematical formulation of kernel PCA and thus they are straightforward to understand. The main contribution of this work is that we firstly suggest a fault identification method especially applicable to process monitoring using kernel PCA. To demonstrate the performance, the proposed method is applied to two simulated processes, one is a simple nonlinear process and the other is a non-isothermal CSTR process. The simulation results show that the proposed method effectively identifies the source of various types of faults.