Flow instabilities in rheotens experiments: Analysis of the impacts of the process conditions through neural network modeling

Tronci, Stefania; Coppola, Salvatore; Bacchelli, Fabio; Grosso, Massimiliano

doi:10.1002/pen.23387

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Preprocessing methods depend on the objective of the study and on the technique used to model data [21,22]. In the present work, the Principal Component Analysis (PCA) has been used [23], that allows representing the variation present in many variables using a smaller number of abstract factors (or principal components: PCs) which are orthogonal to each other and sorted in decreasing order. The number of significant PCs should be in principle equal to the number of factors actually describing the data.…”

Section: Methodsmentioning

confidence: 99%

In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Tronci

Neer²,

Giling³

et al. 2019

Sensors

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The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

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Section: Methodsmentioning

confidence: 99%

In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Tronci

Neer²,

Giling³

et al. 2019

Sensors

Self Cite

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“…A simple description of the data collected in the pilot plant can be attained by means of a continuous-time Hammerstein model [9], where the nonlinear no-memory gain is calculated with a Neural Network (NN) model [10,11]. The time invariant state space representation of a nonlinear continuous-time Hammerstein system (Figure 3) that cascades a static nonlinearity followed by a linear dynamic system may be described by Equation (1)…”

Section: Process Simulatormentioning

confidence: 99%

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

et al. 2018

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Abstract:The application of a multivariable predictive controller to the mixing process for the production of a non-Newtonian fluid is discussed in this work. A data-driven model has been developed to describe the dynamic behaviour of the rheological properties of the fluid as a function of the operating conditions using experimental data collected in a pilot plant. The developed model provides a realistic process representation and it is used to test and verify the multivariable controller, which has been designed to maintain viscosity curves of the non-Newtonian fluid within a given region of the viscosity-vs-shear rate plane in presence of process disturbances occurring in the mixing process.

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Predictive control of an activated sludge process for long term operation

Foscoliano

Vigo

Mulas

et al. 2016

Chemical Engineering Journal

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he application of a multivariable predictive controller to an activated sludge process is discussed in this work. Emphasis is given to the model identification and the long term assessment of the controller efficiency in terms of economical and environmental performances. A recurrent neural network model is developed for the identification problem and the dynamic matrix control is chosen as suitable predictive control algorithm for controlling the nitrogen compounds in the bioreactor. Using the Benchmark Simulation Model No. 1 as virtual platform, different predictive controller configurations are tested and further improvements are achieved by controlling the suspended solids at the end of the bioreactor. Based on the simulation results, this work shows the potentiality of the dynamic matrix control that together with a careful identification of the process, is able to decrease the energy consumption costs and, at the same time, reduce the ammonia peaks and nitrate concentration in the effluent

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Flow instabilities in rheotens experiments: Analysis of the impacts of the process conditions through neural network modeling

Cited by 3 publications

References 30 publications

In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

Predictive control of an activated sludge process for long term operation

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