A novel interval-halving framework for automated identification of process trends

Assessment of Coke Oven Operating State Using Trend Analysis and Information Entropy

In the combustion process of a coke oven, it is crucial to evaluate the operating state to ensure control performance for the stabilization of the coke oven temperature. This paper presents an assessment method for a coke oven operating state based on the analysis of the mechanism. A coke oven, which is an integrator, is categorized into serial subsystems, which include two coking chambers and one combustion chamber. First, the raw gas temperature of every coking chamber is extracted online and is combined with the qualitative trend analysis that yields the feature point of the raw gas temperature. Subsequently, fuzzy method is presented to describe the uncertainty and evaluate the heat level of each subsystem. Finally, a comprehensive assessment of the operating state of the coke oven is performed by combining the weighted contribution of all subsystems, which is expressed by information entropy. Simulations and experiments demonstrate the validity of the method.

On developing a framework for detection of oscillations in data

Oscillation is a phenomenon very commonly observed in systems, ranging from simple ones to complex distributed network. Several techniques have been proposed in the literature for detecting oscillations to study their importance in domains ranging from physiology to climate studies. However, there is a lack of a common framework accommodative of important features of data such as non-stationarity, intermittent oscillations, measurement noise, multimodal oscillations, and the like. In this article, we outline a framework that addresses these challenges, the results of which can then be analyzed along with appropriate knowledge about the underlying system. We present results of an extensive simulation study that establishes the robustness and reliability of the proposed technique and demonstrate its applicability to real datasets in climate and in industrial datasets.

Semi-Qualitative Trend Analysis for the Monitoring of Process Control Loops

This paper first introduces a semi-qualitative methodology to analyze a time trend based on a qualitative description with the addition of quantitative attributes. Using this methodology, it further defines a method to diagnose and qualify a control valve problem in a process control loop. Finally, the defined method of diagnosis successfully applied to several actual data sets from industrial chemical plants.