A statistics based approach to enhancing safety and reliability of the batch-reactor charging operation
Abstract In general there are two critical decisions that must be made in the charging sequence of batch reactors, i.e. target setting and alarm generation. In this paper, a number of statistics-based strategies are proposed to perform these tasks. Specifically, both off-line and on-line target setting procedures are presented and either one of them can be adopted to increase the profit margin of any given batch process without sacrificing reliability. A synthesis method for building optimal alarm logic is also described in detail. The monitoring systems constructed according to this suggested approach are effective in reducing the probability of undetected faulty batches. These techniques have been tested with extensive simulation studies. The results show that the proposed strategies are suitable for application if high-value-added products are manufacture in the plant, which in fact is a prevailing situation of batch processes.
Abstract In this paper data uncertainty in a specific scenario is modeled with Berkson and Classical errors. With consideration of uncertainty three methods within Bayesian framework are presented to update the failure probability with Beta-Binomial model. It shows that the three methods have their posteriors in the same form of weighted Beta distributions, but the weights are different for each method. Approximation to the mixed posteriors has been proposed and demonstrated by computation results. Moreover, comparison and illustration of the three methods are made based on case study and analytical analysis, which suggest that the LO method with Classical error model be more appropriate in similar applications.