An Exploratory Analysis of Biased Learners in Soft-Sensing Frames

Data driven soft sensor design has recently gained immense popularity, due to advances in sensory devices, and a growing interest in data mining. While partial least squares (PLS) is traditionally used in the process literature for designing soft sensors, the statistical literature has focused on sparse learners, such as Lasso and relevance vector machine (RVM), to solve the high dimensional data problem. In the current study, predictive performances of three regression techniques, PLS, Lasso and RVM were assessed and compared under various offline and online soft sensing scenarios applied on datasets from five real industrial plants, and a simulated process. In offline learning, predictions of RVM and Lasso were found to be superior to those of PLS when a large number of time-lagged predictors were used. Online prediction results gave a slightly more complicated picture. It was found that the minimum prediction error achieved by PLS under moving window (MW), or just-in-time learning scheme was decreased up to ~5-10% using Lasso, or RVM. However, when a small MW size was used, or the optimum number of PLS components was as low as ~1, prediction performance of PLS surpassed RVM, which was found to yield occasional unstable predictions. PLS and Lasso models constructed via online parameter tuning generally did not yield better predictions compared to those constructed via offline tuning. We present evidence to suggest that retaining a large portion of the available process measurement data in the predictor matrix, instead of preselecting variables, would be more advantageous for sparse learners in increasing prediction accuracy. As a result, Lasso is recommended as a better substitute for PLS in soft sensors; while performance of RVM should be validated before online application.Comment: Preprin

Predição e controle da qualidade no processo de destilação em batelada com uso de sensor virtual

Over the years, scientific advance has enabled the technological development of society as a whole, and in the industrial processes has not been different. Distillation, a widely used unit operation, still has studies focused on monitoring and optimization because there is a need to improve plant efficiency due to the increased competitiveness. Therefore, research related to the simulation area has increased its importance, and the control of product composition in the distillation processes becomes essential for the success of the operation. With this, the soft sensors are presented as a viable alternative, because they allow the reduction of cost and associates the simplicity with the precision of the results. Thus, a soft sensor was developed and validated based on thermodynamic requirements to predict the ethanol composition in a batch distillation process of a binary mixture. For this, experiments were carried out to understand the quasi-stationary distillation process, with water and absolute ethyl alcohol, where an experimental design was done to evaluate the influence of the initial solution concentration (20% and 30%) and the reflux ratio (total and 1: 2) on the alcohol content of the product. These tests were also used for a pre-calibration of the soft sensor. In addition, batch experiments with water and absolute ethyl alcohol in different initial concentrations of alcohol (2% to 30%) and reflux 1: 2 were performed, and later used in the calibration of the sensor. The sensor validation was done with data from jabuticaba wort, and the results showed that the sensor was able to predict the experimental behavior.Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqAo longo dos anos, o avanço científico tem possibilitado o desenvolvimento tecnológico da sociedade como um todo e, nos processos indústrias não tem sido diferente. A destilação, operação unitária bastante utilizada, ainda apresenta estudos voltados para o monitoramento e otimização, pois, com aumento da competitividade, existe a necessidade de melhorar a eficiência das plantas. Diante disto, pesquisas ligadas a área da simulação têm ganhado cada vez mais relevância e o controle da composição do produto nos processos de destilação tornase essencial para o sucesso da operação. Com isso, os sensores virtuais apresentam-se como alternativa viável, pois possibilitam a redução de custo e associa a simplicidade com a precisão dos resultados. Desta forma, desenvolveu-se e validou-se um sensor virtual fundamentado em requisitos termodinâmicos para predizer a composição de etanol em um processo de destilação em batelada de uma mistura binária. Para isto, foram realizados experimentos com o intuito de compreender o processo de destilação em regime quaseestacionário, com água e álcool etílico absoluto, onde um planejamento experimental foi feito para avaliar a influência da concentração inicial da solução (20% e 30%) e a razão de refluxo (total e 1:2) sobre o teor de álcool do produto. Tais ensaios também foram utilizados para uma pré-calibração do sensor virtual. Além disso, experimentos em regime batelada com água e álcool etílico absoluto em diferentes concentrações iniciais de álcool (2% a 30%) e refluxo 1:2 foram realizados, e posteriormente utilizados na calibração do sensor. A validação do sensor foi feita com dados de um mosto de jabuticaba e os resultados mostraram que o sensor foi capaz de predizer o comportamento experimental.São Cristóvã