Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion

In industry today many products are sold for their efficacy rather than their chemical composition. Many variables (dependent variables), which characterize the quality of the final product in a manufacturing process, can be difficult to measure in real-time. Measurement difficulties can be due to a variety of reasons, including: (1) Reliability of on-line sensors, (2) Lack of appropriate on-line instrumentation. It is often the case that off-line laboratory tests are the only means of determining product quality measurements. However such laboratory analyses introduce delays in the measurement of key performance indicators. This can result in a significant economic loss if the analysed product fails the quality control test. In such situations an improved monitoring system is therefore required to determine product quality online and minimise commercial wastage. To facilitate this, advanced monitoring and control or optimisation techniques require inferred measurements, generated with correlations from readily available process variables (independent variables). Although inferential models are widely used in industry, only a few techniques for inferential model development are discussed in the open literature. This paper therefore will present a comparative evaluation study of the current inferential measurement techniques. An improved systematic approach for the development of inferential models using intelligent and soft computing systems is also highlighted. The proposed approach is designed to address some of the problems that currently exist in the area of inferential modelling through the fusion of statistical and computational intelligence models. A novel method of fusion is also proposed and an industrial case study is then presented to demonstrate the methodology by inferring the 'Anchorage' of polymeric-coated substrates (i.e. Tyvek or paper) in the coating industry. The application on which this methodology is demonstrated is unique. No such work in the literature to date has presented any inferential modelling strategies in the area of the coating industry. This strategy developed through the fusion of statistical and artificial modelling to generate a hybrid inferential measurement system has the potential to significantly improve the quality control monitoring system and reduce the economic loss encountered through the production of off-spec material.

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