Optimization of oil shale pyrolysis

Wen, C. S.; Yen, Teh Fu

doi:10.1016/0009-2509(77)80221-2

Cited by 20 publications

(15 citation statements)

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“…The second application considers the model of a three-tank system that has been commonly used as a benchmark in different monitoring, control and fault detection and diagnosis studies [66,67,68,69]. The third case study involves the model for a shale-oil pyrolysis batch system that has been frequently addressed as example of batch processes dynamic optimization [70,71].…”

Section: Applicationsmentioning

confidence: 99%

“…( ). With this objective, the optimal batch time, , and the optimal temperature profile over the batch time It is worthy to mention that the batch time is set to its optimal value identified in the literature [71], i.e. = 9.3 , while the sampling period is set to 0.093 .…”

Section: Oil Shale Pyrolysismentioning

confidence: 99%

“…66−69 The third case study involves the model for a shale-oil pyrolysis batch system that has been frequently addressed as an example of batch process dynamic optimization. 70,71 As previously mentioned, in all these case studies, two application scenarios will be considered: the first one would mimic a realistic situation where only input−output signals are available for training the models (see Section 3.4), and thus, the FPM is used as the process plant from which these signals are collected. The second scenario assumes that the FPM is available for the application of the proposed DOCE procedure in order to optimally select the training data (see Section 3.3).…”

Section: Applicationsmentioning

confidence: 99%

“…Pyrolysis approximates the natural processing of the organic material in the shale, i.e., kerogen, using higher temperatures to compensate for the geological time frame. 71 Upon heating, kerogen decomposes by consecutive reactions into a benzene-soluble material (pyrolytic bitumen), which, in turn, decomposes to form the final products of oil, gas, and carbonaceous residue on the spent shale: 71 The series of reactions taking place during the process is illustrated in eq 10 where Kr is the kerogen, Pb is the pyrolytic bitumen, Og is oil and gas and Oc is the organic carbon residue. 71 The mathematical model in eq 11 describes the evolution of the concentrations, C Kr , C Pb , C Og , and C Cr , where k i is the specific reaction rate, k i0 is its initial value, E i is the activation energy, R is the gas constant, and T is the temperature that can be manipulated within the range of [698.15 ≤ T ≤ 748.15].…”

Section: Applicationsmentioning

confidence: 99%

“…71 Upon heating, kerogen decomposes by consecutive reactions into a benzene-soluble material (pyrolytic bitumen), which, in turn, decomposes to form the final products of oil, gas, and carbonaceous residue on the spent shale: 71 The series of reactions taking place during the process is illustrated in eq 10 where Kr is the kerogen, Pb is the pyrolytic bitumen, Og is oil and gas and Oc is the organic carbon residue. 71 The mathematical model in eq 11 describes the evolution of the concentrations, C Kr , C Pb , C Og , and C Cr , where k i is the specific reaction rate, k i0 is its initial value, E i is the activation energy, R is the gas constant, and T is the temperature that can be manipulated within the range of [698.15 ≤ T ≤ 748.15]. 70…”

Section: Applicationsmentioning

confidence: 99%

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Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes

Shokry

Baraldi

Zio

et al. 2020

Ind. Eng. Chem. Res.

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This work proposes a methodology for multivariate dynamic modeling and multistep-ahead prediction of nonlinear systems using surrogate models for the application to nonlinear chemical processes. The methodology provides a systematic and robust procedure for the development of data-driven dynamic models capable of predicting the process outputs over long time horizons. It is based on using surrogate models to construct several Nonlinear AutoRegressive eXogenous models (NARX), each one approximating the future behavior of one process output as a function of the current and previous process inputs and outputs. The developed dynamic models are employed in a recursive schema to predict the process future outputs over several time steps (multistep-ahead prediction). The methodology is able to manage two different scenarios: 1) one in which a set of input-output signals collected from the process is only available for training, and 2) another in which a mathematical model of the process is available and can be used to generate specific datasets for training. With respect to the latter, the proposed methodology includes a specific procedure for the selection of training data in dynamic modeling based on Design Of Computer Experiment (DOCE) techniques. The proposed methodology is applied to case studies from the process industry presented in the literature. The results show very high prediction accuracies over long time horizons. Also, thanks to the flexibility, robustness and computational efficiency of surrogate modeling, the methodology allows dealing with a wide range of situations, which would be difficult to address using first principle models.

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Section: Applicationsmentioning

confidence: 99%

Section: Oil Shale Pyrolysismentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes

Shokry

Baraldi

Zio

et al. 2020

Ind. Eng. Chem. Res.

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Optimization of non‐steady‐state operation of reactors

Tremblay

Luus

1989

Can J Chem Eng

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Non‐steady‐state operation of reactors is approached from the optimal control point of view. To find the best type of input, dynamic programming is used. The results show that for some systems, the optimal input is oscillatory in nature as has been found experimentally by others. A modification to conventional dynamic programming is used to avoid the need for interpolation and thus allow high‐dimensional systems to be handled more readily. Three chemical engineering systems of different complexity were considered to test the computational aspect of the approach. In all cases, dynamic programming gives the optimum policy with respect to the period, cycle‐split and amplitude.

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On the optimization of oil shale pyrolysis

Luss

1978

Chemical Engineering Science

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Optimization of oil shale pyrolysis

Cited by 20 publications

References 0 publications

Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes

Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes

Optimization of non‐steady‐state operation of reactors

On the optimization of oil shale pyrolysis

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