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。

Many chemical reaction systems exhibit input/output multiplicity characteristics and non-minimum phase behavior. These inherent characteristics are known to cause limitations in process operation, so it is useful to have some knowledge of these at the early design stage of a chemical reaction process. Focusing on inherently safer designs, this paper addresses a strategy for classifying the process operating region into distinct zones at the early stage of process design, based on stability/instability and minimum/non-minimum phase behavior analysis. The strategy is illustrated by two case studies, where the operating spaces of an isothermal CSTR and an exothermic CSTR are classified into zones with different characteristics. The results provide information that is very important for guiding process design and operation about how the inherent properties of a process change with changes in its operating conditions. (C) 2009 Elsevier B.V. All rights reserved.

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.

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.

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.