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Article
Publication date: 14 February 2025

Xuemei Li, Yuyu Sun, Yansong Shi, Yufeng Zhao and Shiwei Zhou

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote…

3

Abstract

Purpose

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

Design/methodology/approach

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Findings

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

Originality/value

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

Available. Open Access. Open Access
Article
Publication date: 14 May 2024

Yuyu Sun, Yuchen Zhang and Zhiguo Zhao

Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to…

206

Abstract

Purpose

Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.

Design/methodology/approach

Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.

Findings

In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.

Practical implications

The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.

Originality/value

Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.

Details

Marine Economics and Management, vol. 7 no. 1
Type: Research Article
ISSN: 2516-158X

Keywords

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Article
Publication date: 20 September 2024

Ming-Hui Liu, Jianbin Xiong, Chun-Lin Li, Weijun Sun, Qinghua Zhang and Yuyu Zhang

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to…

47

Abstract

Purpose

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

Design/methodology/approach

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

Findings

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

Research limitations/implications

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Originality/value

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Details

Robotic Intelligence and Automation, vol. 44 no. 6
Type: Research Article
ISSN: 2754-6969

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Article
Publication date: 19 November 2021

Fanhua Wu, Yuyu Zhang, Tao Li, Yongfeng Liu, Yi Liu, Zhiang Yuan and Rongjun Qu

The purpose of this study was to prepare carboxylated attapulgite (APT-COOH) and then be used as one of the ligands to prepare metal organic framework (MOF) hybrid materials to…

179

Abstract

Purpose

The purpose of this study was to prepare carboxylated attapulgite (APT-COOH) and then be used as one of the ligands to prepare metal organic framework (MOF) hybrid materials to reduce the cost of MOF materials and improve the dispersed condition of APT. And then the materials were used to enrich anionic dye Congo red from aqueous solution.

Design/methodology/approach

The MOF hybrid materials were designed by means of facile reflux method rather than hydrothermal method, characterized by Thermogravimetric Analysis (TGA), Fourier Transform Infrared (FTIR) Spectrometer and pore structure. The dispersed degree of APT-COOH in the MOF materials was validated according to adsorption efficiency for Congo red.

Findings

Due to introduction of APT-COOH, the microenvironment of the MOF materials changed, leading to different adsorption behaviors. Compared to the MOF material without APT-COOH, the adsorption capacities of the hybridized MOF materials with different amounts of APT-COOH introduced increased by 4.58% and 15.55%, respectively, as the initial concentration of Congo red solution of 300 mg/L. Meantime, hybridized MOF materials were suitable to remove Congo red with low concentration, while the MOF material without APT-COOH was appropriate to enrich Congo red with high concentration.

Research limitations/implications

The microstructure of MOF hybrid materials in detail is the further and future investigation.

Practical implications

This study will provide a method to reduce the cost of MOF materials and a theoretical support to treat anionic dyes from aqueous solution.

Originality/value

APT-COOH was prepared and used as one of the ligands to synthesize MOF material to improve the dispersed degree of APT-COOH and reduce the cost of the MOF materials. The adsorption efficiency was greatly enhanced with low concentration of Congo red solution, and the results indicated that hydrogen bonding, electrostatic interaction, and p-p conjugation were involved in the adsorption process. The prepared MOFs materials exhibited excellent adsorption efficiency, which made the present materials highly promising and potentially useful in practical application as adsorbents to enrich anionic dyes such as Congo red from aqueous solution.

Details

Pigment & Resin Technology, vol. 51 no. 6
Type: Research Article
ISSN: 0369-9420

Keywords

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Article
Publication date: 4 January 2022

Yuyu Hao, Shugang Li and Tianjun Zhang

In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable…

103

Abstract

Purpose

In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law.

Design/methodology/approach

First, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend.

Findings

This approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach.

Originality/value

For the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism.

Details

Assembly Automation, vol. 42 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 5 January 2022

Yuyu Hao, Shugang Li and Tianjun Zhang

This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on…

127

Abstract

Purpose

This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection.

Design/methodology/approach

First, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure.

Findings

The sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform.

Originality/value

The proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.

Available. Open Access. Open Access
Article
Publication date: 13 February 2024

Amer Jazairy, Emil Persson, Mazen Brho, Robin von Haartman and Per Hilletofth

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into…

4694

Abstract

Purpose

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Design/methodology/approach

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

Findings

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

Research limitations/implications

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

Practical implications

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

Originality/value

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

Details

The International Journal of Logistics Management, vol. 36 no. 7
Type: Research Article
ISSN: 0957-4093

Keywords

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