A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger. (1st July 2023)

• Record Type: Journal Article
• Authors: Shang, Gang; Xu, Liyun; Tian, Jinzhu; Cai, Dongwei; Xu, Zhun; Zhou, Zhuo
• Is Part Of: Energy. Volume 274(2023)
• Publication Date: 2023-07-01
• Subjects: Construction optimization -- Fuel consumption -- Machine learning -- Cutter suction dredger -- Real-time prediction -- Energy efficiency; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2023.127326
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Energy saving in the process of engineering construction is an indispensable measure to achieve industrial low carbon emissions. To reduce fuel consumption in the construction process of engineering vessels, a real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity is proposed. A cutter suction dredger (CSD) is taken as a case study. Based on its working principle, a series of potential monitoring features are determined. In addition, details of data collection are presented. Then, the original data obtained are cleaned based on human experience. The Hilbert Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) was used to filter out irrelevant and redundant factors to achieve feature dimensionality reduction, followed by data standardization based on the robust scaler function. Five machine learning models, including gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), random forest regression (RFR), support vector regression (SVR) and super learner based on stacked strategy (SLBSS), are used to train and test offline data on the basis. The Taguchi test method is used to optimize the hyperparameters of these models. In addition, an online data collection, processing and analysis framework is proposed. The online data collected in real time are used to predict the fuel consumption level based on this framework. Finally, feature importance is analyzed. Aiming at the minimum fuel consumption per unit productivity ( FC pup ), specific and feasible optimization suggestions are made for CSD construction. The results show that machine learning models can predict the fuel consumption level effectively. The first seven important features should be used for prediction. The productivity, lubricating oil pressure of the pump gearbox in the cabin and slurry density are the three most important factors. Improving the parameters related to productivity and reducing the sailing speed, reamer motor voltage and washing pressure of reamer bearings can improve fuel efficiency, which is beneficial to energy savings and emission reduction. Engineering construction shows that this strategy can reduce the fuel consumption of the CSD by approximately 20%. Highlights: The construction optimization strategy reduces ships fuel consumption by about 20%. The number of features used for real-time FC pup prediction should be more than six. Real-time FC pup prediction through online construction data is feasible. Improving productivity-related parameters can effectively reduce FC pup .