A generalised approach to process state estimation using hybrid artificial neural network/mechanistic models
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A review of machine learning methods applied to structural dynamics and vibroacoustic
2023, Mechanical Systems and Signal ProcessingMachine-learning-based state estimation and predictive control of nonlinear processes
2021, Chemical Engineering Research and DesignCitation Excerpt :In Porru et al. (2000), a neural network model was developed to represent the reaction kinetics and was coupled with the first-principles model to obtain a hybrid model that was successfully applied within the EKF. It is reported that hybrid models can not only augment the region of operation, but also provide a more general modeling framework that can build models faster and need no process insights (Wilson and Zorzetto, 1997). Machine learning models can be utilized in model-based controllers to predict future states.
Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization
2016, Chinese Journal of Chemical EngineeringCitation Excerpt :Recently, hybrid modeling approaches have been investigated as an attractive alternative to develop fermentation process models [6]. The hybrid model of a fermentation process commonly consists of a set of nonlinear differential equations that incorporates the available priori knowledge about the process under consideration, and some empirical models that each estimates one of the unknown variables in the differential equations [7–13]. Moreover, hybrid models have been shown, in many applications to fermentation processes, to have better properties than pure empirical models [9,10,12]; they have better generalization ability, are easier to analyze and interpret, and require significantly fewer training examples.
Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey
2015, Expert Systems with ApplicationsReview and classification of recent observers applied in chemical process systems
2015, Computers and Chemical EngineeringCitation Excerpt :At the same time, many researchers have also shown their interest in applying pure AI techniques, such as the artificial neural network (ANN), fuzzy logic and expert systems for predicting the parameters in difficult-to-model systems (Bahar and Ozgen, 2010; Beigzadeh and Rahimi, 2012; Brudzewski et al., 2006; de Canete et al., 2012; Delrot et al., 2012; Hussain et al., 2002; Patnaik, 1997; Porter Ii and Passino, 1995; Singh et al., 2005, 2007). AI algorithms have also been merged with each other for estimation purposes with different types of formulations (Arauzo-Bravo et al., 2004; Chitanov et al., 2004; Chuk et al., 2005; Li et al., 2002; Ng and Hussain, 2004; Sivan et al., 2007; Wei et al., 2007, 2010; Wilson and Zorzetto, 1997; Yang and Yan, 2011; Yetilmezsoy et al., 2011). However, the problems with AI-based observers, especially in online estimations, are issues related to robustness and stability, which may be difficult to resolve if the data collected for the AI design do not cover the whole range of the operating region for the observer (Himmelblau, 2008; Wang et al., 2011; Wei et al., 2010).
Hybrid semi-parametric modeling in process systems engineering: Past, present and future
2014, Computers and Chemical EngineeringCitation Excerpt :In case that the hybrid semi-parametric model is serial and uses at-time available measurements the same requirements formulated above for the soft-sensor case hold. Standard corrector schema that found application comprise the EKF (Porru et al., 2000; van Lith et al., 2002; Wilson & Zorzetto, 1997) or a DDI filter (Feil et al., 2004). For a certain class of serial hybrid semi-parametric models, a state transformation technique can be applied for (i) inference of unmeasured states, (ii) on-line state correction and (iii) ANN weight adaptation (Georgieva & de Azevedo, 2009).