The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a powerful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in efficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the presence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be estimated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of decision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.

A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows that automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI.

Process safety, occupational health and environmental issues are ever increasing in importance in response to heightening public concerns and the resultant tightening of regulations. The process industries are addressing these concerns with a systematic and thorough process hazards analysis (PHA) of their new, as well as existing facilities. Given the enormous amounts of time, effort and money involved in performing the PHA reviews, there exists considerable incentive for automating the process hazards analysis of chemical process plants. In this paper, we review the progress in this area over the past few years. We also discuss the progress that has been made in our laboratory on the industrial application of intelligent systems for operating procedure synthesis and HAZOP analysis. Recent advances in this area have promising implications for process hazards analysis, inherently safer design, operator training and real-time fault diagnosis.

To improve the learning capability of HAZOP expert systems, a new learning HAZOP expert system called PetroHAZOP has been developed based on the integration of case-based reasoning (CBR) and ontology that can help automate "non-routine" HAZOP analysis. PetroHAZOP consists of four modules including case base module, CBR engine module, knowledge maintenance module and user graphical interface module. Within the case base, HAZOP analysis knowledge is represented as cases which are organized with a hierarchical structure. Similarity-based case retrieval algorithm is also depicted to find the closestmatching cases. In order to enhance the case retrieval, a new set of ontologies for CBR-based HAZOP analysis is created by integration of existing ontologies reported in literature. Finally the application of PetroHAZOP is demonstrated by two case studies of industrial processes.

故障诊断是保障化工过程安全、平稳进行的一个重要工具。主成分分析法作为典型的故障诊断方法，已经广泛应用于各类化工过程的故障诊断，但在复杂过程的故障类别判断上还存在不足。而人工免疫系统的对于自我-非我的识别能力有助于对故障类别的判断，并且其良好的自适应、自学习能力，有助于在诊断过程中对系统的完善和改进。本文将主成分分析法与人工免疫系统结合，建立了一个新的混合故障诊断系统，实现对于化工过程故障的早期诊断。并用Honeywell公司的UniSim平台建立了一个动态的化工过程模型，对该诊断系统进行了验证。

Adsorption behavior of carbon dioxide confined in pillared clays is analyzed by using constant pressure Gibbs ensemble Monte Carlo (GEMC) method. In our simulation, 1-site and 3-site models are used to represent carbon dioxide. At the 1-site model, carbon dioxide is described as a Lennard–Jones (LJ) sphere, while at the 3-site model, carbon dioxide is modeled as a three-sites linear chain represented by EPM2 potential considering the quadrapolar effect. The potential model from Yi et al. for pillared clays is used to emphasize its quasi two-dimensional structure. Comparing the calculated results from the 1-site and the 3-site models at T=228.15 and 258.15 K, we observe that the adsorption amount from the two models is the same basically. However, the local density presents a significant difference, because the shoulder in the main peak near the wall from 3-site model can reflect the orientation of carbon dioxide. Accordingly, in the systematical investigation to explore the effect of porosity and pore width on the adsorption of carbon dioxide in pillared clays, the 3-site model was only used. We observe that for a narrow pore of H=1.02 nm, each isotherm shape displays type I curve, suggesting that it is not inflected by the porosity. However, for the larger pores of H=1.70 and H=2.38 nm, the increase of the porosity alters the shape of adsorption isotherms from a simple linear relation to the first order jump, indicating that the porosity is of very important factor to affect adsorption and phase behavior of fluids confined in pillared clays. The excess adsorptions of carbon dioxide at supercritical temperatures of T=323.15 and 348.15 K are also investigated. We find that the maximum exists for each excess isotherm, and the optimal pressure corresponding to the maximum increases with the pore width. However, the porosity has no significant effect on the optimal pressure.

故障诊断是保证化工过程稳定性和安全性的重要技术。本文结合动态时间规整算法提出了一个基于人工免疫系统的间歇化工过程故障诊断方法，并成功应用于青霉素发酵仿真过程的故障诊断。诊断结果显示，该方法可以满足间歇过程的在线动态故障诊断要求，并且通过自学习可以对未知故障进行诊断。

As modern chemical plants are becoming more complex and bigger in scale, the associated chance of things going wrong is also increasing rapidly. Due to the flammable, explosive, toxic and corrosive nature of chemical process, any single accident may trigger a major catastrophe that brings tremendous environmental, social and economical loss. In order to prevent any accident from happening, hazard and operability (HAZOP) analysis has been brought in to monitor chemical process and provide early warning for signs of accident. However, most existing HAZOP is carried out manually, and there are always obstacles in terms of cost overrun and incompleteness of the analysis. To address the difficulties in current HAZOP method, this paper proposes a signed digraph (SDG)-based HAZOP analysis method. It is used to identify the most likely operating mistakes that may cause certain process variable deviating from its normal value, which is the main source of safety concern. A case study on polyvinyl chloride (PVC) plant is presented to demonstrate the effectiveness of SDG-based HAZOP analysis method in providing complete analysis result.

Dealing with multidimensional problems has been the ÔÔbottle-neckÕÕ for implementing wavenets to process systems engineering. To tackle this problem, a novel multidimensional wavelet (MW) is presented with its rigorously proven approximation theorems. Taking the new wavelet function as the activation function in its hidden units, a new type of wavenet called multidimensional non-orthogonal non-product wavelet-sigmoid basis function neural network (WSBFN) model is proposed for dynamic fault diagnosis. Based on the heuristic learning rules presented by authors, a new set of heuristic learning rules is presented for determining the topology of WSBFNs. The application of the proposed WSBFN is illustrated in detail with a dynamic hydrocracking process. (1998 Elsevier Science Ltd. All rights reserved.

Herein, two models, the general rate model taking into account convection, axial dispersion, external and intra-particle mass transfer resistances and particle size distribution (PSD) and the artificial neural network model (ANN) were developed to describe solanesol adsorption process in packed column using macroporous resins. First, Static equilibrium experiments and kinetic experiments in packed column were carried out respectively to obtain experimental data. By fitting static experimental data, Langmuir isotherm and Freundlich isotherm were estimated, and the former one was used in simulation coupled with general rate model considering better correlative coefficients. The simulated results showed that theoretical predictions of general rate model with PSD were well consistent with experimental data. Then, a new model, the ANN model, was developed to describe present adsorption process in packed column. The encouraging simulated results showed that ANN model could describe present system even better than general rate model. At last, by using the predictive ability of ANN model, the influence of each experimental parameter was investigated. Predicted results showed that with the increases of particle porosity and the ratio of bed height to inner column diameter (ROHD), the breakthrough time was delayed. On the contrary, an increase in feed concentration, flow rate, mean particle diameter and bed porosity decreased the breakthrough time.