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Batch processes are important in chemical industry, in which operators usually play a major role and hazards may arise by their inadvertent acts. In this paper, based on hazard and operability study and concept of qualitative simulation, an automatic method for adverse consequence identification for potential maloperation is proposed. The qualitative model for production process is expressed by a novel directed graph. Possible operation deviations from normal operating procedure are identified systematically by using a group of guidewords. The proposed algorithm is used for qualitative simulation of batch processes to identify the effects of maloperations. The method is illustrated with a simple batch process and a batch reaction process. The results show that batch processes can be simulated qualitatively and hazards can be identified for operating procedures including maloperations. After analysis for possible plant maloperations, some measures can be taken to avoid maloperations or reduce losses resulted from maloperations.

Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development of a software environment that can automate the validation activity. This paper is concentrated on the pretreatment of the model validation. We use the validation scenarios and standard sequences generated by well-established SDG model to validate the trends fitted from the simulation model. The results are helpful to find potential problems, assess possible bugs in the simulation model and solve the problem effectively. A case study on a simulation model of boiler is presented to demonstrate the effectiveness of this method.

Qualitative algebraic equations are the basis of qualitative simulation, which are used to express the dynamic behavior of steady-state continuous processes. When the values and operation of qualitative variables are redefined, qualitative algebraic equations can be transformed into signed direct graphs, which are frequently used to predict the trend of dynamic changes. However, it is difficult to use traditional qualitative algebra methods based on artificial trial and error to solve a complex problem for dynamic trends. An important aspect of modern qualitative algebra is to model and characterize complex systems with the corresponding computer-aided automatic reasoning. In this study, a qualitative affection equation based on multiple conditions is proposed, which enables the signed direct graphs to describe complex systems better and improves the fault diagnosis resolution. The application to an industrial case shows that the method performs well.

In the past 30 years, signed directed graph (SDG), one of the qualitative simulation technologies, has been widely applied for chemical fault diagnosis. However, SDG based fault diagnosis, as any other qualitative method, has poor diagnostic resolution. In this paper, a new method that combines SDG with qualitative trend analysis (QTA) is presented to improve the resolution. In the method, a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model. Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consistent paths found in the previous step. New consistency rules based on qualitative trends are used to find the real causes from the candidate causes. The resolution can be improved. This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA. The implementation of SDG-QTA based fault diagnosis is done using the integrated SDG modeling, inference and post-processing software platform. Its application is illustrated on an atmospheric distillation tower unit of a simulation platform. The result shows its good applicability and efficiency.

Hazard and Operability (HAZOP) analysis is a systematic procedure for determining the abnormal causes of process deviations from normal behavior and their adverse consequences in a chemical plant. A computer-aided HAZOP analysis software platform based on Signed Directed Graph (SDG) called SDG-HAZOP has been developed recently. While HAZOP analysis is generally applied only to continuous process plants, it can be generalized to include batch process systems as well, as shown in this paper. The main thrust of this paper, however, is to propose an efficient methodology utilizing SDG for the HAZOP analysis in batch process. This approach has been applied to an industrial case. The results show that it is more accurate and more comprehensive when compared with the conventional methods.

Significant research has been done in the past 30 years to use signed directed graph (SDG) for process fault diagnosis. However, due to non-unified SDG models for control loops, highly complex and integrated nature of chemical processes, few of SDG based methods has been applied in the real chemical processes. In this paper, SDG based deep knowledge modeling and bidirectional inference algorithms are introduced. With the algorithms a SDG based fault diagnosis and decision support system is developed and applied in fault diagnosis for an atmospheric distillation unit of a large-scale refining plant in China. The results prove that the SDG based fault diagnosis and decision support system can not only arrive at the fundamental requirement of diagnosis: correctness, completeness and real-timed, but also provide decision support for operators to decrease the possibility of unscheduled shut-down or more serious accident due to abnormal situation.

Soft sensor software based on ANN (artificial neural network) using BP or RBF was developed to estimate unmeasured variables such as product quality online. Some important topics including how to determine the delay time, how to simulate the dynamic system were discussed and solved. We applied a 3 layers BP network to identify the delay time of nonlinear system, feedback output variables to input layer, and weight of all the input variables to describe dynamic characteristics of the system. This makes the ANN soft sensor reflect truly both the static and dynamic characteristics of the system and provide more adaptability

The structure of hardware and software of the training simulation system, the dynamic mathematical model based on first principle analysis and simulation results of fluid catalytic cracking unit (FCCU) were introduced and discussed. We used the fuzzy synthetic judgment algorithm to the operation evaluation subsystem, which is very important for operator training. This training simulation system has been applied for training plant operators with satisfied results. Many operators have been trained on this system and both their confidence and proficiency for operating are dramatically improved. With the usage of fuzzy synthetic judgment, operating results are given out even more scientifically and reasonably and this also promotes the applications of the training simulation system

The dynamic model of fluid catalytic cracking unit (FCCU) based on first principle analysis was discussed and built. It was simplified by spatial discretization to be applied in the simulation system to meet the real time requirement. Dynamic simulation is a key technique in safety science and safety engineering field which can be used for process hazard assessment, hazard identification, safety control system design and operating personnel training. Based on dynamic model and simulation, we proposed a new structure of a hybrid fault diagnosis system of FCCU which integrates signed directed graph (SDG), artificial neural network (ANN) together with dynamic simulation. Dynamic model and simulation are the foundation and play very important roles in this hybrid fault diagnosis system.