Abstract
Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity.
Highlights
Batch processes are widespread in the industrial manufacturing of high value-added products, such as specialty chemicals, pharmaceuticals, agricultural goods and biochemicals
We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments that can exploit multivariate statistical techniques
We provide a very short overview of the multivariate statistical techniques used in this study, namely principal component analysis (PCA) and projection on to latent structures (PLS)
Summary
Batch processes are widespread in the industrial manufacturing of high value-added products, such as specialty chemicals, pharmaceuticals, agricultural goods and biochemicals. Compared to their continuous counterparts, batch processes are relatively easier to set up, more flexible through their ability to handle variations in feedstock and product specifications, and can be used for the manufacturing of multiple products in a single multipurpose plant. Even when the manufacturing recipe is fully automated (which is not always the case in an industrial setting), variability in the raw materials, operating conditions of each unit, and initial status of the equipment can make it difficult to consistently meet the strict quality specifications the final product is subject to [2,3].
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