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Historical Process Data (historical + process_data)

Distribution by Scientific Domains
Chemistry100%

Selected Abstracts

Nonstationary fault detection and diagnosis for multimode processes

AICHE JOURNAL, Issue 1 2010
Jialin Liu
Abstract Fault isolation based on data-driven approaches usually assume the abnormal event data will be formed into a new operating region, measuring the differences between normal and faulty states to identify the faulty variables. In practice, operators intervene in processes when they are aware of abnormalities occurring. The process behavior is nonstationary, whereas the operators are trying to bring it back to normal states. Therefore, the faulty variables have to be located in the first place when the process leaves its normal operating regions. For an industrial process, multiple normal operations are common. On the basis of the assumption that the operating data follow a Gaussian distribution within an operating region, the Gaussian mixture model is employed to extract a series of operating modes from the historical process data. The local statistic T2 and its normalized contribution chart have been derived for detecting abnormalities early and isolating faulty variables in this article. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source]

Product transfer between plants using historical process data

AICHE JOURNAL, Issue 10 2000
Christiane M. Jaeckle
Based on the concepts laid out in an earlier article (Jaeckle and MacGregor, 1998), this paper defines the problem of moving the production of a particular product grade from a plant A to another plant B when both plants have already produced a similar range of grades. Since the two plants may differ in size, configurations, and so on, the process conditions required to produce any given product grade may be very different in the two plants. How historical process data on both plants may be utilized to assist in this problem is investigated. A multivariate latent variable method is proposed that uses data from both plants to predict process conditions for plant B for a grade previously produced only in plant A. The approach is illustrated by a simulation example. [source]

An Adaptive Recipe Implementation in Case-Based Formalism for Abnormal Condition Management

CHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 12 2005
D. Rizal
Abstract This paper deals with accurate recipe implementation for abnormal condition management in a batch process using a case-based reasoning (CBR) approach. A set of new problems can be solved by reusing proven process solutions. The proposed system integrates quantitative and qualitative parameters for adaptation of cases. A novel methodology to generate accurate recipes and to adapt to the processes is introduced during normal and abnormal conditions. In particular, the differences between current conditions and the references (recipes) should be managed to prevent any hazardous conditions arising. The processes are evaluated using their similarity to the past cases. This intelligent approach distinguishes plausible cases, generates accurate recipes, and adapts to new situations. The aim is to use the offline historical process data and safety related information in order to propose changes and adjustments in the processes. [source]