Simulation and Advanced Control of the Continuous Biodiesel Production Process

Brásio, Ana S. R.; Romanenko, Andrey; Fernandes, Natércia C. P.

doi:10.1007/978-3-319-74086-7_6

Cited by 3 publications

(10 citation statements)

References 14 publications

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“…In spite of the already highlighted differences between the system described in Section and that of Brásio et al, some elements and features are in fact common. Hence, the deterministic model was adapted/augmented to account for the dissimilarities.…”

Section: First-principles Modelmentioning

confidence: 87%

“…The apparatus described by Brásio et al was taken as the base system and was further complemented with additional equipment, as rendered in Figure . This extended setup constitutes the process to be emulated by the simulator.…”

Section: Physical Systemmentioning

confidence: 99%

“…Only after having dried up the remnant water, biodiesel is finally obtained. Refer to Brásio et al for further details although there are some differences in the system operation. It is noteworthy that the level of the interface in the present model is a state-controlled variable rather than a constant value as in Brásio et al…”

Section: Physical Systemmentioning

confidence: 99%

“…From mass and energy balances to the reactor unit, the composition and temperature in the reactor vessel are described according to the following system of differential equations with the total amount of substance inside the reactor given by and the reaction rates of the different components computed from where the reaction rate of each of the three steps is with, according to the Arrhenius Law The molar concentrations of the components relate to the molar fraction counterparts according to and, finally, the molar flow rate leaving the reactor unit is obtained by Refer to Brásio et al for explanatory details. The numerical values of the parameters are provided in Appendix A.…”

Section: First-principles Modelmentioning

confidence: 99%

“…This approach allows us to balance the exactness of the iterative rigorous liquid–liquid model with the computational effort and time that are required to obtain a solution. The interested reader is referred to Brásio et al for further technical details.…”

Section: First-principles Modelmentioning

confidence: 99%

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Mechanistic Modeling and Simulation for Process Data Generation

Fernandes

Romanenko

Reis

2019

Ind. Eng. Chem. Res.

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The paper presents a new benchmark simulator for testing advanced fault detection and diagnosis methods as well as control and optimization schemes, among other training and educational applications: BDsim. The context is the biodiesel production through the transesterification reaction of oil by methanol, in continuous mode. This process was accurately modeled and simulated, including a wide variety of upsets that may occur at different levels: process, sensors, and actuators. Each process fault is modeled in a rigorous way to make the simulator a realistic test bed for advanced datadriven algorithms. Furthermore, faulty scenarios not covered by current simulators were incorporated, such as intermittent faults and equipment degradation phenomena. The user has freedom to select the faults to simulate as well as their timing and magnitude. The code is fully compatible with both Matlab and Octave. BDsim is publicly available, registered under the GNU General Public License.

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Section: First-principles Modelmentioning

confidence: 87%

Section: Physical Systemmentioning

confidence: 99%

Section: Physical Systemmentioning

confidence: 99%

Section: First-principles Modelmentioning

confidence: 99%

Section: First-principles Modelmentioning

confidence: 99%

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Mechanistic Modeling and Simulation for Process Data Generation

Fernandes

Romanenko

Reis

2019

Ind. Eng. Chem. Res.

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The digital twin in Industry 4.0: A wide‐angle perspective

Kenett

Bortman

2021

Quality & Reliability Eng

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The move towards advanced manufacturing and Industry 4.0 is fed by increased demand for speeding up innovation, increasing flexibility, improving maintenance, and becoming more customized while saving on the total cost of operations. This is accompanied by increased dependence on virtual product and process development, data‐driven processes, and product knowledge. Other characteristics, affecting modern design, include big data intelligence in product, process, and maintenance. New technologies that empower data analytics include added manufacturing, flexible manufacturing, robotics, sensor technology, smart value/supply chains, and industrial information backbones. This paper is about surrogate models, also called digital twins, that provide an important complementary capacity to physical assets. Digital twins capture past, present, and predicted behavior of physical assets. Digital twin models are updated periodically to represent the current state of physical assets. This distinguishes digital twins from conventional simulations in that sensor data can continuously feed them. The type of curated information on the state of physical asset's history depends on how digital twins are used. For example, if a digital twin is used for fault classification, the history captured is operational data from equipment in healthy and faulty states. We provide here a review of digital twins, with an emphasis on Industry 4.0 applications.

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Rigorous Dynamic Modeling and Simulation of an Industrial Liquid–Liquid Extraction Packed Column for Biodiesel Washing

Pinho,

Braz,

Granjo

et al. 2023

Ind. Eng. Chem. Res.

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This work focuses on developing a dynamic model of a packed bed column for biodiesel washing. Its development was motivated by increasing problems in these columns when higher amounts of waste cooking oil and other low-quality fatty materials are incorporated as raw materials. This incorporation causes the formulations of raw oil to change significantly, requiring process flexibility and adequate control systems to maintain the product within strict quality standards and prevent operational problems. A detailed rate-based model of a packed column is implemented in gPROMS ModelBuilder, accounting for mass transfer and hydrodynamics phenomena such as holdup, flooding, and phase inversion. The model is compared to data from an industrial unit. The simulation results are in good agreement with the typical concentration values measured in the industrial unit. Using the developed model, the influence of operating parameters and the presence of contamination on the column’s behavior are studied.

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Simulation and Advanced Control of the Continuous Biodiesel Production Process

Cited by 3 publications

References 14 publications

Mechanistic Modeling and Simulation for Process Data Generation

Mechanistic Modeling and Simulation for Process Data Generation

The digital twin in Industry 4.0: A wide‐angle perspective

Rigorous Dynamic Modeling and Simulation of an Industrial Liquid–Liquid Extraction Packed Column for Biodiesel Washing

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