Control of the Moisture Content of Milk Powder Produced in a Spouted Bed Dryer Using a Grey-Box Inferential Controller

Vieira, Gustavo Nakamura Alves; Freire, Fábio Bentes; Freire, José Teixeira

doi:10.1080/07373937.2015.1075999

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…This approach combines a partial model based on the main principles of the process with a Multilayer Feed-Forward Neural network (MFFN) that is able to estimate the parameters that cannot be measured (or are difficult to be quantified from first principles). This is the case of the model developed by [257] and used to infer the moisture content of milk powder during its drying process. It was composed by one term which considered mass and energy balances and a second term which was estimated by an ANN.…”

Section: New Approaches: Neural Network Modellingmentioning

confidence: 99%

Modelling of Spouted and Spout-Fluid Beds: Key for Their Successful Scale Up

et al. 2017

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The development of robust mathematical models could provide the necessary tools for a more rapid, efficient, and reliable spouted bed technology development. Computer simulations can be very useful to aid this design and scale-up process: firstly, they can contribute to obtain a fundamental insight into their complex dynamic behavior by understanding the elementary physical principles such as drag, friction, dissipation etc.; secondly, the simulations can be used as a design tool where the ultimate goal is to have a numerical model with predictive capabilities for gas-particle flows at engineering scale. Clearly, one single simulation method will not be able to achieve this goal, but a hierarchy of methods modelling phenomena on different length and time scales can achieve this. The most fruitful approach will be when they are simultaneously followed, so that they can mutually benefit from each other. In this sense, this paper presents a review of the current state of the art of modelling on spouted and spout-fluid beds through an analysis of recent literature following a multiscale approach (molecular and particle, lab, plant and industrial scale). The main features of the different scales together with their current limits are discussed and specific topics are highlighted as paths that still need to be explored. In summary, the paper aims to define the theoretical setline and the basis of improvement that would lead to a robust multiscale model with solid links between micro and macroscopic phenomena. If done with the correct balance between accuracy and computational costs it will gear SB towards their reliable and successful implementation.

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Section: New Approaches: Neural Network Modellingmentioning

confidence: 99%

Modelling of Spouted and Spout-Fluid Beds: Key for Their Successful Scale Up

et al. 2017

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“…Cubillos et al [37] developed a serial GB for estimation and control of moisture content in a direct fish-meal rotary dyer where ANN was used as a BB model. Vieira et al [38] developed a serial GB model for prediction and control of the moisture content of milk powder produced in a spouted bed dryer. ANN was used as BB technique.…”

Section: Food Processingmentioning

confidence: 99%

“…Industry Application Category Process GB Type Target BB Type [32][33][34][35][36]92] Iron and steelmaking estimation and control "pickling process", "continuous casting", "hot strip mill" "serial", "parallel", "combined" "concentration of hydrochloric acid", "tundish temperature", "scale breaker entry temperature", "drying rate" "Taylor series", "PLS and RF", "ANN" [37][38][39] Food industry estimation and control 'fish drying process", "milk drying process", "whey separation" serial "drying rate", "moisture contents", "membrane fouling" "ANN", "exponential static membrane resistance function" [45][46][47][48][49][50][51][52][53][54]93] Chemical, biochemical, and pharmaceutical "estimation and optimization", "estimation and control" "fermentation extraction", "twin screw extruder ", "extrusion", "mold cooling", "acetone-butanol ethanol fermentation process", "MP fermentation", "fed-batch fermentation", "evaporation plant"…”

Section: Papermentioning

confidence: 99%

“…The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators.Applications of GB models are found in a variety of domains in process industries such as design, estimation, control, and monitoring. They are applied across the process industries such as iron and steel making [32][33][34][35][36], food processing [37][38][39], oil and gas processing [40-44], chemical, biochemical, and pharmaceutical [45-54], power plants [55][56][57], water treatment [58][59][60], material processing and energy materials [61-64] and industrial robots [65][66][67].The aim of the current study is to do a comprehensive review of the fundamentals of GB soft sensors, their applications in the process industry and prospects in the era of big data analytics; to the best of the authors' knowledge, no extensive review that addresses all such dimensions is reported in the literature. GB soft sensors application in selected process industries were investigated.…”

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confidence: 99%

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Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

et al. 2020

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Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

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“…In an inferential control system, the nonlinear estimator is proposed to the feedback control to estimate the moisture content due to changes in the inlet temperature and the outlet temperature. Neural network was used in this research as it has been successfully used as a soft sensor (estimator) of moisture content of dried milk [42]. It is also due to fact that neural network is able to handle highly nonlinear correlation with multiple input output mapping.…”

Section: Neural Network (Nn) Estimator Developmentmentioning

confidence: 99%

Nonlinear Model-Based Inferential Control of Moisture Content of Spray Dried Coconut Milk

Abdullah

Taip

Kamal

et al. 2020

Foods

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The moisture content of a powder is a parameter crucial to be controlled in order to produce stable products with a long shelf life. Inferential control is the best solution to control the moisture content due to difficulty in measuring this variable online. In this study, fundamental and empirical approaches were used in designing the nonlinear model-based inferential control of moisture content of coconut milk powder that was produced from co-current spray dryer. A one-dimensional model with integration of reaction engineering approach (REA) model was used to represent the dynamic of the spray drying process. The empirical approach, i.e., nonlinear autoregressive with exogenous input (NARX) and neural network, was used to allow fast and accurate prediction of output response in inferential control. Minimal offset (<0.0003 kg/kg) of the responses at various set points indicate high accuracy of the neural network estimator. The nonlinear model-based inferential control was able to provide stable control response at wider process operating conditions and acceptable disturbance rejection. Nevertheless, the performance of the controller depends on the tuning rules used.

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Control of the Moisture Content of Milk Powder Produced in a Spouted Bed Dryer Using a Grey-Box Inferential Controller

Cited by 17 publications

References 27 publications

Modelling of Spouted and Spout-Fluid Beds: Key for Their Successful Scale Up

Modelling of Spouted and Spout-Fluid Beds: Key for Their Successful Scale Up

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

Nonlinear Model-Based Inferential Control of Moisture Content of Spray Dried Coconut Milk

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