Lecture notes in control and information sciences

Fault diagnosis is important for ensuring chemical processes stability and safety. The strong nonlinearity and complexity of batch chemical processes make such diagnosis more difficult than that for continuous processes. In this paper, a new fault diagnosis methodology is proposed for batch chemical processes, based on an artificial immune system (AIS) and dynamic time warping (DTW) algorithm. The system generates diverse antibodies using known normal and fault samples and calculates the difference between the test data and the antibodies by the DTW algorithm. If the difference for an antibody is lower than a threshold, then the test data are deemed to be of the same type of this antibody’s fault. Its application to a simulated penicillin fermentation process demonstrates that the proposed AIS can meet the requirements for online dynamic fault diagnosis of batch processes and can diagnose new faults through self-learning. Compared with dynamic locus analysis and artificial neural networks, the proposed me...

Fault Diagnosis of Batch Chemical Processes Using a Dynamic Time Warping (DTW)-Based Artificial Immune System

Automata-based operating procedure for abnormal situation management in batch processes

Online diagnosis has been considered as an important measure for improving operational safety in many batch chemical plants. Specifically, the state transition behaviors of all hardware items (components) in the given batch process and their failure mechanisms are modeled systematically with automata. The system model is then assembled by connecting the component models on the basis of a generic hierarchical structure. A “diagnoser” can be constructed accordingly for the purpose of determining various qualitative and quantitative performance indices. Guided by these indices, two performance enhancement approaches can be effectively applied: (1) installing additional sensors which are not included in the piping and instrumentation diagram (P&ID) and (2) executing extra operation steps which are not specified in the sequential function chart (SFC). Three examples are presented in this paper to demonstrate the feasibility of the proposed approach.

An automaton-based approach to evaluate and improve online diagnosis schemes for multi-failure scenarios in batch chemical processes

Systematic synthesis of untimed operating procedures has always been considered as an important design issue for batch chemical processes. An automaton-based method is developed in the present study to perform this task automatically. On the basis of the proposed methodical model-building principles, two distinct types of automata can be constructed to characterize the plant behaviors and control specifications, respectively. An admissible supervisor can be produced by applying the parallel composition operation with these models. For the purpose of identifying the most efficient operation procedures, the supervisor can then be integrated with a set of auxiliary automata to set the operation target(s) and, also, to impose upper limits on the total numbers of actuator actions and operation steps. Three examples are presented to demonstrate the feasibility and correctness of the proposed approach.

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An automata-based approach to synthesize untimed operating procedures in batch chemical processes

The CPM (Critical Path Method) is a network-based approach for project management. This method identifies the longest path, which allows us to find the critical path that must be shortened so that the completion time of the whole project can be shortened. However, considering uncertainty in CPM is not straightforward. In this paper, we consider an optimization problem for stochastic CPM problems, where task durations are expressed as discrete histograms obtained from historical operation data, that maximizes the probability that all tasks are completed within a given completion time by improving the task durations on the critical path. We propose two reformulations of the problem as a mixed-integer linear programming problem: one based on tasks, and the other based on paths. In addition, we propose an iterative method to solve the problem efficiently by reducing the number of binary variables. Finally, we demonstrate efficiency of our proposed methods in some case studies.

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Application of critical path method to stochastic processes with historical operation data

A batch process is a typical concurrent system in which multiple interacting tasks are carried out in parallel on several batches at the same time. A major difficulty in designing a batch control system is the lack of modeling techniques. This paper aims at developing a method of constructing batch control system models in a hierarchical manner and operating batch processes using the constructed models. For this purpose, it first defines process and plant specifications described by partial languages, next presents a procedure for constructing hierarchical Petri net based models, and states the verification of models based on reachability analysis. It also discusses the detection of faults and conflicts in batch processes based on place-invariant analysis.

Control of Batch Processes Based on Hierarchical Petri Nets

Batch control is characterized by procedural control that is realized according to operating procedures to accomplish tasks. However, operating procedure synthesis takes considerable time and effort as the complexity of a batch process increases. To solve the operating procedure synthesis problem, a modeling technique is needed to represent the concurrent dynamics of a batch process. This paper aims at developing a method to synthesize operating procedures and procedural controllers for batch processes. For this purpose, it first views a batch control system as a discrete event system and next uses hierarchical Petri nets to represent information about process, plant, and schedule. It then presents a method to synthesize operating procedures. It also discusses the verification of synthesized operating procedures and last presents a method to synthesize procedural controllers.

Synthesis of Operating Procedures and Procedural Controllers for Batch Processes Based on Petri Nets

Integration of Fault Analysis and Interlock Controller Synthesis for Batch Processes

A batch process is a discontinuous and concurrent process which is suitable for multi-product, small-sized production. The distinctive feature of a batch process is that various decision making processes, such as scheduling, design, operation, etc. are strongly connected with each other. Interaction among these processes is necessary to dynamically and flexibly cope with a variety of unplanned events. This paper aims at presenting a batch scheduling technique based on Petri net models and showing the possibilities of integration between scheduling and design of batch processes. For this purpose, it first views the behavior of a batch operating system as a discrete event system and presents a Petri net model to be used for scheduling, design and operation. It next formulates batch scheduling problems based on Petri net partial languages, proposes their solution technique and last discusses the integration between scheduling and design of batch systems.

Susumu Hashizume

Papers

Application of critical path method to stochastic processes with historical operation data

Control of Batch Processes Based on Hierarchical Petri Nets

Synthesis of Operating Procedures and Procedural Controllers for Batch Processes Based on Petri Nets

Integration of Fault Analysis and Interlock Controller Synthesis for Batch Processes

Integration between Scheduling and Design of Batch Systems Based on Petri Net Models