Myths and misconceptions of data-driven methods: Applications to process safety analysis

Wen, He; Khan, Faisal; Amin, Md. Tanjin; Halim, Syeda Zohra

doi:10.1016/j.compchemeng.2021.107639

Cited by 15 publications

(2 citation statements)

References 41 publications

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“…In recent years, data-driven methods have been applied successfully in various disciplines, and their scheme has been widely applied in solving various problems. In the assessment of process safety, approximately 500 studies conducted between 1990 and 2020 were sampled [24]. Recent data-driven approaches in the maritime field generally focus on the assessment of ship performance and management from data collected during voyages [25].…”

Section: Data-driven Approaches In the Maritime Industrymentioning

confidence: 99%

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

et al. 2023

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Energy efficiency and operational safety practices on ships have gained more importance due to the rules set by the International Maritime Organization in recent years. While approximately 70% of the fuel consumed on a commercial ship is utilized for the propulsion load, a significant portion of the remaining fuel is consumed by the auxiliary generators responsible for the ship’s onboard load. It is crucial to comprehend the impact of the electrical load on the ship’s generators, as it significantly assists maritime operators in strategic energy planning to minimize the chance of unexpected electrical breakdowns during operation. However, an appropriate handling mechanism is required when there are massive datasets and varied input data involved. Thus, this study implements data-driven approaches to estimate the load of a chemical tanker ship’s generator using a 1000-day real dataset. Two case studies were performed, namely, single load prediction for each generator and total load prediction for all generators. The prediction results show that for the single generator load prediction of DG1, DG2, and DG3, the decision tree model encountered the least errors for MAE (0.2364, 0.1306, and 0.1532), RMSE (0.2455, 0.2069, and 0.2182), and MAPE (17.493, 5.1139, and 7.7481). In contrast, the deep neural network outperforms all other prediction models in the case of total generation prediction, with values of 1.0866, 2.6049, and 14.728 for MAE, RMSE, and MAPE, respectively.

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Section: Data-driven Approaches In the Maritime Industrymentioning

confidence: 99%

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

et al. 2023

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“…6 Data-driven methods such as principal component analysis (PCA), independent component analysis, and k-nearest neighbor are commonly used in data analysis, which can realize fault detection and diagnosis in the industrial production process. 7 Support vector machine (SVM) and artificial neural network (ANN) can be used to solve nonlinear problems in industrial data. 8 Combining PCA with a quantitative operational risk assessment model can solve the problem of false alarms.…”

Section: Introductionmentioning

confidence: 99%

Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

et al. 2023

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Solvent deasphalting (SDA) is a complex multiscale continuous process. The operation mode of the SDA process is not considered in the related data-driven model. Therefore, this paper proposes a time lag process prediction model with multiple operation modes to solve the above problem. First, based on random forests, the relative importance of initial input variables in the SDA process on DAO yield and Conradson carbon residual are studied and features are selected according to the results. Then, the stack denoising autoencoder (SDAE) is used to reconstruct the data and obtain the nonlinear mapping information of hidden layers of SDAE and achieve feature dimension reduction. SDAE can improve clustering accuracy of fuzzy c-means, and the operation mode of SDA process is accurately divided. Long short-term memory (LSTM) is used to establish a multicondition LSTM model. Compared with the traditional LSTM model, the multicondition LSTM model has a higher prediction accuracy with R 2 > 0.95. The sensitivity analyses of the properties of feed and operating conditions on DAO yield are consistent with the principle of two-phase countercurrent extraction in the SDA process. In addition, the benchmark test of the Tennessee Eastman process shows that the proposed method is also effective in the fault detection of other processes. Because the multicondition LSTM can predict the future process measurement data according to operating mode, it can better avoid the false alarm problem and predict the fault earlier.

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Data-Driven Approaches for Accident Analysis in Sociochemical Systems

Gholamizadeh,

Zarei,

Yazdi

et al. 2024

Studies in Systems, Decision and Control

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Myths and misconceptions of data-driven methods: Applications to process safety analysis

Cited by 15 publications

References 41 publications

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

Data-Driven Approaches for Accident Analysis in Sociochemical Systems

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