Operational optimization of crude oil distillation systems using artificial neural networks

Ochoa-Estopier, Lluvia M.; Jobson, Megan; Smith, Robin

doi:10.1016/b978-0-444-59520-1.50055-5

Cited by 10 publications

(10 citation statements)

References 7 publications

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“…Unit operations (e.g., chemical synthesis and separation) are basic steps of chemical processes (Green & Perry, 2008). AI has been applied to improve the performance of specific unit operations, such as combustion (Zheng et al., 2009; Zhou et al., 2005), distillation (Ochoa‐Estopier et al., 2013; Osuolale & Zhang, 2016, 2017), evaporation (Verma et al., 2017), compression (Qi et al., 2018), and reaction (Fernandes, 2006; Mosavi et al., 2019; Schweidtmann et al., 2018). Some studies focused on technical indicators such as yields (Fernandes, 2006; Geng et al., 2016; Keyvanloo et al., 2012; Schweidtmann et al., 2018), while the others focused on risk‐related performance (Guo et al., 2009; Jaderi et al., 2019; Qi et al., 2018) and intelligent control of unit operations (Ji et al., 2016).…”

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

confidence: 99%

“…Most studies focused on reducing the costs of chemical production through AI applications. Three studies assessed cost reduction using different indicators, as shown in Table 2 (Ochoa‐Estopier et al., 2013; Osuolale & Zhang, 2016, 2017). We identified six studies that applied AI to chemical processes and/or chemical plants.…”

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

confidence: 99%

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

confidence: 99%

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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“…Within the last decade, there has been a push toward the use of ANNs in chemical engineering (Himmelblau, 2008), where they have found use as estimators and as part of simulation of processes (Pareek et al, 2002;Nelofer et al, 2012;Feng et al, 2013;Mohd Ali et al, 2015). As an example, ANNs have been employed for modeling and prediction in traditional processes such as in distillation, fuel production and in the design of fuel cells (Ochoa-Estopier et al, 2012;Feng et al, 2013;Baroi and Dalai, 2014). They have been also extensively used to simulate difficult processes such as the influence of parameters in catalyst preparation through experimental data (Gunay et al, 2012), or even to extract such rules from existing literature (Odabasi et al, 2014).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Bradford

Schweidtmann

Zhang

et al. 2018

Computers & Chemical Engineering

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Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model's validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering.

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“…Once the material balance between conversion and separation units is established, additional devices are introduced during the process design step, such as heat exchangers or pressure-changing devices, e.g., compressors, pumps, and pipes (Chen et al, 2011a, b). At this stage, the optimization focuses on heat integration and its interaction with the rest of the process (Ochoa-Estopier et al, 2013). The purposes of the process design are to obtain the final and detailed flowsheet of the process, calculate precise heat and mass balances, and perform an economic evaluation of the process.…”

Section: Introductionmentioning

confidence: 99%

From conceptual design to process design optimization: a review on flowsheet synthesis

Montastruc

Belletante

Pagot

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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This paper presents the authors’ perspectives on some of the open questions and opportunities in Process Systems Engineering (PSE) focusing on process synthesis. A general overview of process synthesis is given, and the difference between Conceptual Design (CD) and Process Design (PD) is presented using an original ternary diagram. Then, a bibliometric analysis is performed to place major research team activities in the latter. An analysis of ongoing work is conducted and some perspectives are provided based on the analysis. This analysis includes symbolic knowledge representation concepts and inference techniques, i.e., ontology, that is believed to become useful in the future. Future research challenges that process synthesis will have to face, such as biomass transformation, shale production, response to spaceflight demand, modular plant design, and intermittent production of energy, are also discussed.

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Operational optimization of crude oil distillation systems using artificial neural networks

Cited by 10 publications

References 7 publications

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

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

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

From conceptual design to process design optimization: a review on flowsheet synthesis

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