Prediction of industrial debutanizer column compositions using data-driven ANFIS- and ANN-based approaches

Sun

Journal of Environmental and Public Health

et al. 2022

Environmental problem is an international problem that transcends national boundaries and develops into regional and global environmental pollution and ecological problems. Facing the increasing environmental pollution, the international community has successively formulated many relevant environmental pollution prevention laws, but the world situation is complicated after all, environmental problems still emerge endlessly, and the protection of environmental rights has become the consensus of the international community. Environmental right is an integral part of human rights, and protecting environmental right is the concrete expression and proper meaning of protecting human rights. Using international criminal law to protect environmental rights will play a positive role in global environmental protection. As with the development of computer technology, the research of machine learning has gradually transferred to the field of social science, especially the judicial field. While sentencing is a crucial part of environmental crime, this paper studies the sentencing of environmental rights cases from the perspective of international criminal law and uses Convolutional Neural Networks (CNN) to determine the sentencing of environmental rights cases. Through the experiment on the Integrated Database (IDB) dataset, the results show that the introduction of CNN improves the effect of the sentencing term prediction model and the fine prediction model significantly. The CNN-based model scored 91.6542 in the sentencing term prediction model and 90.8890 in the fine prediction model.

Section: Experiments and Results Analysismentioning

confidence: 99%

International Criminal Law Protection of Environmental Rights and Sentencing Based on Artificial Intelligence

Sun

Journal of Environmental and Public Health

et al. 2022

“…Anmol et al. 76 inspected the performance of ANFIS using the debutanizer data, in which LPG was produced as top stream and naphtha was produced as bottom stream as shown in Fig. 7.…”

Section: Application To Ai and ML In Process Industriesmentioning

confidence: 99%

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Khan

Taqvi

2022

ChemBioEng Reviews

The field of machine learning has proven to be a powerful approach in smart manufacturing and processing in the chemical and process industries. This review provides a systematic overview of current state of artificial intelligence and machine learning and their applications in textile, nuclear power plant, fertilizer, water treatment, and oil and gas industries. Moreover, this study reveals the current dominant machine learning methods, pre and post processing of models, increased utilization of machine learning in terms of fault detection, prediction, optimization, quality control, and maintenance in these sectors. In addition, this review gives the insight into the actual benefits and impact of each method, and complications in their extensive deployment. Finally in the current impressive state, challenges, future development in terms of algorithm and infrastructure aspects are highlighted.

“…This is challenging for engineers since the distillation columns are complex and highly unpredictive [51]. Application of machine learning has been proposed to handle such a task; however, not many works have been seen due to limited available data [20], [36], [52]. Fatima et al [20] used ANFIS to estimate the top and bottom compositions in a distillation column.…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

“…Application of machine learning has been proposed to handle such a task; however, not many works have been seen due to limited available data [20], [36], [52]. Fatima et al [20] used ANFIS to estimate the top and bottom compositions in a distillation column. Even with limited samples, the ANFIS model provided good prediction accuracy.…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Vuttipittayamongkol¹,

Tung

Elyan

2021

IEEE Access

A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders' concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.