Energy performance analysis of continuous processes using surrogate models

Beisheim, Benedikt; Rahimi-Adli, Keivan; Krämer, Stefan; Engell, Sebastian

doi:10.1016/j.energy.2019.05.176

Cited by 23 publications

(10 citation statements)

References 29 publications

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“…They are used to describe the behavior of a system that, for various reasons, is not suited to be built knowledge-based. They are used in a broad range of use cases in the energy domain: starting from the calculation and optimization of energy savings (Beisheim et al 2019;Nagpal et al 2019;Vazquez-Canteli et al 2019) and the replacement of specific simulation models Dimitrov 2019) over surrogate models for (micro)grids (Baumann et al 2019;Balduin 2018;Grundel et al 2019) to the use in uncertainty and reliability assessment (Blank and Lehnhoff 2014;Slot et al 2020;Steinbrink 2016). This list is far from complete and there are also other approaches such as in Gerster (2018) who use surrogate models to build a decoder function abstracting from technical system specifications.…”

Section: Related Workmentioning

confidence: 99%

Evaluating different machine learning techniques as surrogate for low voltage grids

2020

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The transition of the power grid requires new technologies and methodologies, which can only be developed and tested in simulations. Especially larger simulation setups with many levels of detail can become quite slow. Therefore, the number of possible simulation evaluations decreases. One solution to overcome this issue is to use surrogate models, i. e., data-driven approximations of (sub)systems. In a recent work, we built a surrogate model for a low voltage grid using artificial neural networks, which achieved satisfying results. However, there were still open questions regarding the assumptions and simplifications made. In this paper, we present the results of our ongoing research, which answer some of these questions. We compare different machine learning algorithms as surrogate models and exchange the grid topology and size. In a set of experiments, we show that algorithms based on linear regression and artificial neural networks yield the best results independent of the grid topology. Furthermore, adding volatile energy generation and a variable phase angle does not decrease the quality of the surrogate models.

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Section: Related Workmentioning

confidence: 99%

Evaluating different machine learning techniques as surrogate for low voltage grids

2020

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“…The proposed method was applied to define an energy baseline of a process, therefore distance between the current energy consumption and the best demonstrated practice indicated the operational improvement potential. 15 While ML-based predictive analytics has gained a great deal of attention in the petrochemical industry over the last decade, it has not been studied sufficiently in prediction and improving the energy consumption of gas treating plants. This justifies the undertaking of this study to propose a comprehensive data and modeling framework based on ML tools to predict the steam consumption of a gas sweetening process (GSP) of such plants.…”

Section: Introductionmentioning

confidence: 99%

“…Beisheim et al presented a modeling method to find the best historically observed state of the current operation of a propylene oxide production. The proposed method was applied to define an energy baseline of a process, therefore distance between the current energy consumption and the best demonstrated practice indicated the operational improvement potential 15 . While ML‐based predictive analytics has gained a great deal of attention in the petrochemical industry over the last decade, it has not been studied sufficiently in prediction and improving the energy consumption of gas treating plants.…”

Section: Introductionmentioning

confidence: 99%

Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches

2020

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This study proposes a comprehensive data processing and modeling framework for building high-accuracy machine learning model to predict the steam consumption of a gas sweetening process. The data pipeline processes raw historical data of this application and identifies the minimum number of modeling variables required for this prediction in order to ease the applicability and practicality of such methods in the industrial units. On the modeling end, an empirical comparison of most of the state-of-the-arts regression algorithms was run in order to find the best fit to this specific case study. The ultimate goal is to leverage this model to identify the achievable energy conservation opportunity in such plants. The historical data for this modeling was collected from a gas treating plant at South Pars Gas Complex for 3 years from 2017 to 2019. This data gets passed through a multistage data processing scheme that conducts multicollinearity analysis and model-based feature selection. For model selection, a wide range of regression algorithms from different classes of regressor have been considered. Among all these methods, the Gradient Boosting Machines model outperformed the others and achieved the lowest crossvalidation error. The results show that this model can predict the steam consumption values with 98% R-squared accuracy on the holdout test set. Furthermore, the offline analysis demonstrates that there is a potential of 2% energy saving, equivalent to 24 000 metric tons of annual steam consumption reduction, which can be achieved by mapping the underperforming energy consumption states of the unit to the expected performances predicted by the model.

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“…This study focused on identifying best demonstrated practices for different operating regimes for EE assessment. 11 Gong et al identified several modes of operation via k-means method in ethylene production process. For this purpose, they considered the working conditions of production data as the input variables in the k-means algorithm, and showed that by clustering data in groups of reference operating states, they were able to assess EE precisely in that case use.…”

Section: Introductionmentioning

confidence: 99%

“…Beisheim et al applied k‐means algorithm to select representative points and identify characteristic points for operation in a propylene oxide production plant. This study focused on identifying best demonstrated practices for different operating regimes for EE assessment 11 . Gong et al identified several modes of operation via k‐means method in ethylene production process.…”

Section: Introductionmentioning

confidence: 99%

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

2020

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This study proposes a data-driven operational control framework using machine learning-based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi-stage framework is composed of the following steps: (a) a clustering algorithm based on Density-Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high-accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data-driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm 3 of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.

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Energy performance analysis of continuous processes using surrogate models

Cited by 23 publications

References 29 publications

Evaluating different machine learning techniques as surrogate for low voltage grids

Evaluating different machine learning techniques as surrogate for low voltage grids

Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

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