Prediction of the natural gas consumption in chemical processing facilities with genetic programming

KovaăźIăź, Miha; Dolenc, Franjo

doi:10.1007/s10710-016-9264-x

Cited by 12 publications

(8 citation statements)

References 37 publications

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“…Historic energy demand [34,37,39,40,49,[71][72][73]112,116,117,136,150,163,175,[189][190][191][192]254,363,407,427,437,446,447] Weather data [22,37,39,40,112,136,150,163,175,191,254,363,407,427,437,446,447] Calendar data [39,73,112,136,150,446,447] Demographic or economic data [34,…”

Section: Metaheuristicmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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Section: Metaheuristicmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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“…We identified six studies that applied AI to chemical processes and/or chemical plants. Two of them focused on reducing the economic penalty from the imbalance between the planned and consumed natural gas (Kovačič et al., 2016; Kovačič & Dolenc, 2016). Other studies applied AI to decrease the operating costs related to energy (Jahromi et al., 2018), capital costs (Cecchini et al., 2012), and raw material costs (specifically the cost of crude oil consumption) (Han et al., 2019).…”

Section: Review Of Ai Applications In the Chemical Industry And Their...mentioning

confidence: 99%

Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework

Ming-yang

Lan

Yao

2021

J of Industrial Ecology

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Artificial intelligence (AI) is an emerging technology that has great potential in reducing energy consumption, environmental burdens, and operational risks of chemical production. However, large‐scale applications of AI are still limited. One barrier is the lack of quantitative understandings of the potential benefits and risks of different AI applications. This study reviewed relevant AI literature and categorized those case studies by application types, impact categories, and application modes. Most studies assessed the energy, economic, and safety implications of AI applications, while few of them have evaluated the environmental impacts of AI, given the large data gaps and difficulties in choosing appropriate assessment methods. Based on the reviewed case studies in the chemical industry, we proposed a conceptual framework that encompasses approaches from industrial ecology, economics, and engineering to guide the selection of performance indicators and evaluation methods for a holistic assessment of AI's impacts. This framework could be a valuable tool to support the decision‐making related to AI in the fundamental research and practical production of chemicals. Although this study focuses on the chemical industry, the insights of the literature review and the proposed framework could be applied to AI applications in other industries and broad industrial ecology fields. In the end, this study highlights future research directions for addressing the data challenges in assessing AI's impacts and developing AI‐enhanced tools to support the sustainable development of the chemical industry.

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“…For the analysis, we also used a hybrid system, where the four methods mentioned above were connected, namely RF [40], SVM [41], GP [42], and NN [43] of the intelligent system, into one Euler graph method of machine learning (Figure 8). Ensemble methods are learning algorithms that build a series of classifiers and then classify new data points, summarizing the results of their predictions.…”

Section: Analysis Of Geomorphometric Parametersmentioning

confidence: 99%

Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans

Babič¹,

Petrovič

Sodnik

et al. 2021

Remote Sensing

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Alluvial (torrential) fans, especially those created from debris-flow activity, often endanger built environments and human life. It is well known that these kinds of territories where human activities are favored are characterized by increasing instability and related hydrological risk; therefore, treating the problem of its assessment and management is becoming strongly relevant. The aim of this study was to analyze and model the geomorphological aspects and the physical processes of alluvial fans in relation to the environmental characteristics of the territory for classification and prediction purposes. The main geomorphometric parameters capable of describing complex properties, such as relative fan position depending on the neighborhood, which can affect their formation or shape, or properties delineating specific parts of fans, were identified and evaluated through digital elevation model (DEM) data. Five machine learning (ML) methods, including a hybrid Euler graph ML method, were compared to analyze the geomorphometric parameters and physical characteristics of alluvial fans. The results obtained in 14 case studies of Slovenian torrential fans, validated with data of the empirical model proposed by Bertrand et al. (2013), confirm the validity of the developed method and the possibility to identify alluvial fans that can be considered as debris-flow prone.

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Prediction of the natural gas consumption in chemical processing facilities with genetic programming

Cited by 12 publications

References 37 publications

Modeling Energy Demand—A Systematic Literature Review

Modeling Energy Demand—A Systematic Literature Review

Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework

Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans

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