Data-driven low-carbon transformation management for manufacturing enterprises: an eco-efficiency perspective.

The low-carbon transformation of manufacturing enterprises is considered to be imperative to achieve carbon neutrality. Therefore, we propose a data-driven strategy to achieve a low-carbon transformation of manufacturing enterprises from an eco-efficiency perspective. Following the collection of input (energy, materials, equipment, R&D, and services) and output (waste and products) data from production systems of manufacturing enterprises, an ecological efficiency model of manufacturing enterprise production system was constructed from the perspective of carbon emissions, thus allowing the quantitative evaluation of the ecological efficiency of the production system. Furthermore, a "measurable, evaluable, and optimized" low-carbon transformation and upgrading method for manufacturing enterprise production system was established. Finally, through the production practice data of an enterprise from 2017 to 2021, the feasibility and effectiveness of this method were verified. The results show that this method can effectively improve the ecological efficiency of enterprises by 3.6% and reduce waste emissions by 12%. Our study provides new tools for improving the ecological efficiency of manufacturing systems, along with theoretical and methodological support to manufacturing enterprises for low-carbon transformation.

Multi-response assessment for carbon emission and hardening effect in laser surface quenching.

Laser surface quenching (LSQ) is gaining popularity in engineering applications, but it generates non-negligible carbon emissions. However, existing research mostly focuses on quenching performance. Little attention has been paid to carbon emissions of LSQ process. In this study, we build an experimental platform including fiber laser system (IPG YLR-4 kW) and carbon emission measurement system for a synergistic study of environmental impacts and processing quality in LSQ. Based on the L16 (43) Taguchi matrix, LSQ experiments are conducted on the shield disc cutter. The influences of laser power, scanning speed, and defocusing distance on carbon emissions and hardening effects are studied. The carbon emission efficiency of LSQ is analyzed and compared with the competitive technology. The geometry and the maximum average hardness (MAH) of LSQ high-hardness zone (HHZ) are studied. A comprehensive evaluation considering carbon emissions and hardening effects is conducted. The results show that the maximum value of carbon emission is 1.4 times the minimum value. The maximum depth and width of HHZ are respectively 0.507 and 3.254 mm. The maximum MAH is 3.5 times the hardness of base metal. Compared to the average experimental responses, the experiment with the highest comprehensive score respectively increases by 26.4%, 17.1%, and 30.3% in depth, width, and MAH of HHZ, and reduces by 5.8% in carbon emissions.

A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems.

With the increasing severity of environmental problems, low-carbon development has become an inevitable choice. Nowadays, low-carbon green sustainable development is influenced by a variety of factors such as social, environmental, technological, and economic development levels, making its development complex, which in turn imposes challenges on decision-makers. In this context, the application of multi-criteria decision-making (MCDM) in different areas of sustainable development engineering has become a hot topic. Although many reviews of MCDM techniques already exist, there is a lack of holistic review efforts on MCDM in the field of low-carbon transport and green logistics. Considering these shortcomings in the state of the art, this paper systematically reviews more than 190 papers from 2010 to 2022, constructs a general structure of MCDM techniques for this research topic, provides a comprehensive review and analysis of it, and clarifies the current practices. Furthermore, future directions for the development of MCDM techniques for green logistics and low-carbon transportation systems are presented as well.

Hybrid evolutionary algorithm for stochastic multiobjective disassembly line balancing problem in remanufacturing.

With the development of the industrial economy and the accelerated renewal of products, many end-of-life products (EOL) have been generated to pollute our environment. This fact highlights the importance of recycling and remanufacturing EOL products as an active research topic. An efficient disassembly line is one solution for improving the remanufacturing and recycling processes of EOL products while reducing the environmental pollution. Although many optimization models and intelligent algorithms were developed to address the disassembly line balancing problem (DLBP), uncertainty was ignored by them. To alleviate the drawbacks of uncertainty for the disassembly operation, this study proposes a stochastic multi-objective optimization model for the DLBP minimizing the disassembly idle rate, smoothness, and energy consumption generated during the operation under uncertain operation time. Another novelty of this paper is to present an improved version of the northern goshawk optimization algorithm using a stochastic simulation method to solve the proposed model. The feasibility of the proposed model and the applicability of the developed algorithm are shown by two extensive examples. Finally, the performance of the proposed algorithm is revealed by a comparison with recent and state-of-the-art algorithms from the literature.

Environment-oriented disassembly planning for end-of-life vehicle batteries based on an improved northern goshawk optimisation algorithm.

Due to environmental pollution and resource shortages, the electric vehicle industry has been developing swiftly, and the market demand for batteries, as an essential part of electric vehicles, has also surged. Proper disassembly of end-of-life vehicle batteries (ELV batteries) is necessary to achieve the integrity and closure of their life cycle, promote the development of green remanufacturing, effectively reduce the pollution of the environment caused by metal ion leakage, and reduce people's dependence on natural resources to a certain extent. To schedule the disassembly operations of ELV batteries more rationally and further promote their disassembly quality and efficiency, this paper proposes a dual-objective disassembly sequence planning (DSP) optimisation model, which aims to minimise the hazard index and energy cost during ELV battery disassembly operations. Since the proposed model is a complex NP-hard optimisation problem, this study develops an efficient metaheuristic algorithm for solving this model based on the northern goshawk optimisation algorithm. The main algorithm adds two types of discrete recombination operators and a local search operator. At the same time, the predatory behaviour of the goshawk is optimised by combining the characteristics of the disassembly sequence planning problem to improve its performance. Finally, the disassembly of the battery of a Tesla Model 1 is used as a case study to demonstrate the effectiveness and feasibility of the proposed method.

Carbon/Basalt Fibers Hybrid Composites: Hybrid Design and the Application in Automobile Engine Hood.

The low-velocity impact properties and the optimal hybrid ratio range for improving the property of hybrid composites are studied, and the application of hybrid composites in automobile engine hoods is discussed in this paper. The low-velocity impact properties of the hybrid composite material are simulated under different stacking sequences and hybrid ratios by finite element simulation, and the accuracy of the finite element model (FEM) is verified through experiments. Increasing the proportion of carbon fiber (CF) in the hybrid layer and placing the basalt fiber (BF) on the compression side can improve the energy absorption capacity under low-velocity impact loads. CF/BF hybrid composite hoods are optimized based on the steel hood and the low-velocity impact performance of the hybrid composite. The BCCC layer absorbs the most energy under low-velocity impact loads. Compared with CFRP, the energy absorbed under 10 J and 20 J impact energy is increased by 26.1% and 14.2%, respectively. Through the low-velocity impact properties of hybrid composites, we found that placing BF on the side of the load and keep the ratio below 50%, while increasing the proportion of CF in the hybrid laminate can significantly improve the property of the hybrid laminate. The results show that the stiffness and modal properties of the hybrid composite can meet the design index requirements, and the pedestrian protection capability of the hood will also increase with the increase in the proportion of BF.

Codification and Prospect of China's Codification of Environmental Law from the Perspective of Global Environmental Governance.

Global environmental governance is the fundamental way to solve the human environmental crisis. With China as the world's largest emitter of greenhouse gases, the development of China's environmental law is a key component of global environmental governance. In order to better realize the construction of an ecological civilization, the compilation of China's Environmental Code has been officially put on the work schedule of the legislature. The compilation of the code is a sincere action, showing that China has taken the initiative to assume its own responsibility for environmental governance. In the past 50 years, China's environmental legislation has achieved a great leap forward: from nothing to something, from something to something more comprehensive. Aside from this progress, defects such as the internal imbalance of the environmental law system, the backwardness of some environmental legislation ideas, and the inability of environmental legislation and its academic research to fully match China's national conditions also exist. With the helping hands of conditions and times, it is most appropriate for China to start the compilation of the Environmental Code now. Environmental Codes such as the Swedish Environmental Code, the French Environmental Code and the German Environmental Code (Draft of the Committee of Experts) provide many empirical references for the compilation of China's Environmental Code. China will make important an contribution to world environmental governance again-an Environmental Code in line with international standards while maintaining native characteristics.

Capacitated multi-objective disassembly scheduling with fuzzy processing time via a fruit fly optimization algorithm.

This work proposes a capacitated fuzzy disassembly scheduling model with cycle time and environmental cost as parameters, which has broad applications in remanufacturing and many other production systems. Disassembly scheduling is not always given accurately as a time quota in a production system, particularly in the obsolete product remanufacturing process. It is important to study novel models and algorithms based on uncertainty processing time to solve uncertainty disassembly scheduling problems. In this paper, a mixed-integer mathematical programming model is proposed to minimize the cycle time and environmental cost, whilst a metaheuristic approach based on a fruit fly optimization algorithm (FOA) is developed to find a fuzzy disassembly scheduling scheme. To estimate the effectiveness of the proposed method, the proposed algorithm is tested with different size cases of product disassembly scheduling. Furthermore, experiments are conducted to compare with other multi-objective optimization algorithms. The computational results demonstrate the proposed algorithm outperforms other algorithms on computational efficiency and applicability to different problems. Finally, a case study is described to illustrate the proposed method. The main contribution of this current work shows the proposed algorithm to solve the problem of disassembly scheduling in an uncertain environment practically and efficiently.

Modeling and scheduling for remanufacturing systems with disassembly, reprocessing, and reassembly considering total energy consumption.

As one of the mainstream development directions of remanufacturing industry, remanufacturing system scheduling has become a hot research topic recently. This study regards a scheduling problem for remanufacturing systems where end-of-life (EOL) products are firstly disassembled into their constituent components, and next these components are reprocessed to like-new states. At last, the reprocessed components are reassembled into new remanufactured products. Among various system configurations, we investigate a scheduling problem for the one with parallel disassembly workstations, several parallel flow-shop-type reprocessing lines and parallel reassembly workstations for the objective of minimize total energy consumption. To address this problem, a mathematical model is established and an improved genetic algorithm (IMGA) is proposed to solve it due to the problem complexity. The proposed IMGA adopts a hybrid initialization method to improve the solution quality and diversity at the beginning. Crossover operation and mutation operation are specially designed subject to the characteristics of the optimization problem. Besides, an elite strategy is combined to gain a faster convergence speed. Numerical experiments are conducted and the results verify the effectiveness of the scheduling model and proposed algorithm. The work can assist production managers in better planning a scheduling scheme for remanufacturing systems.

A hybrid computational intelligence approach for bioremediation of amoxicillin based on fungus activities from soil resources and aflatoxin B1 controls.

Nowadays, releasing the Emerging Pollutants (EPs) in the nature is one of the main reasons for many health and environmental disasters. Amoxicillin as an antibiotic is one of the EPs and categorized as the Endocrine Disrupting Compounds (EDCs) in hazardous materials. Accumulation of amoxicillin in the soil bulk increases the cancer risk, drug resistances and other epidemiological diseases. Hence, the soil bioremediation of antibiotics can be a solution for this problem which is more environmental-friendly system. This study technically creates a bio-engine setup in soil bulk for remediation of amoxicillin based on Aspergillus Flavus (AF) activities and Removal Percentage (RP) of amoxicillin with Aflatoxin B1 Generation (AG) controls. The main novelty is to propose a hybrid computational intelligence approach to do optimization for mechanical and biological aspects and to predict the behavior of bio-engine's effective mechanical and biological features in an intelligent way. The optimization model is formulated by the Central Composite Design (CCD) which is set by the Response Surface Methodology (RSM). The prediction model is formulated by the Random Forest (RF), Adaptive Neuro Fuzzy Inference System (ANFIS) and Random Tree (RT) algorithms. According to the experimental practices from real soil samples in different times and places, concentration of amoxicillin and Aflatoxin B1 are set equal to 25 mg/L (ppm) and 15 µg/L (ppb). Likewise, the outcomes of experiments in CCD-RSM computations are evaluated by curve fitting comparisons between linear, 2FI, quadratic and cubic polynomial equations with considering to regression coefficient and predicted regression coefficient values, ANOVA and optimization by sequential differentiation. Based on the results of CCD-RSM, the RP performance in the optimum conditions is measured around 86% and in 25 days after runtime, the RP and AG are balanced in the safe mode. The proposed hybrid model achieves the 0.99 accuracy. The applicability of the research is done using real field evaluations from drug industrial park in Mashhad city in Iran. Finally, a broad analysis is done and managerial insights are concluded. The main findings of the present research are: (I) with application of bioremediation from fungus activities, amoxicillin amounts can be control in soil resources with minimum AG, (II) ANFIS model has the best accuracy for smart monitoring of amoxicillin bioremediation in soil environments and (III) based on the statistical assessments Aeration Intensity and AF/Biological Waste ratio are most effective on the amoxicillin removal percentage.

A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian Palm oil industry.

One of the major challenges of the supply chain managers is to select the best suppliers among all possible ones for their business. Although the research on the supplier selection with regards to green, sustainability or resiliency criteria has been contributed by many papers, simultaneous consideration of these criteria in a fuzzy environment is rarely studied. Hence, this study proposes a fuzzy decision framework to investigate the sustainable-resilient supplier selection problem for a real case study of palm oil industry in Malaysia. Firstly, the resilient-based sustainable criteria are localized for the suppliers' performance evaluation in palm oil industry of Malaysia. Accordingly, 30 criteria in three different aspects (i.e. general, sustainable and resilient) are determined by statistical tests. Moreover, a hyper-hybrid model with the use of FDEMATEL (fuzzy decision-making trial and evaluation laboratory), FBWM (fuzzy best worst method), FANP (fuzzy analytical network process) and FIS (fuzzy inference system), simultaneously is developed to employ their merits in an efficient way. In this framework, regarding the outset, the relationships among the criteria/sub-criteria are obtained by FDEMATEL method. Then, initial weights of the criteria/sub-criteria are measured by FBWM method. Next, the final weights of criteria/sub-criteria considering the interrelationships are calculated by FANP. Finally, the performance of the suppliers is evaluated by FIS method. To show the applicability of this hybrid decision-making framework, an industrial case of palm oil in Malaysia is presented. The findings indicate the high performance of the proposed framework in this concept and identify the most important criteria including the cost in general aspects, resource consumption as the most crucial sustainable criterion and agility as the most important resilient criterion.

Cadmium and copper heavy metal treatment from water resources by high-performance folic acid-graphene oxide nanocomposite adsorbent and evaluation of adsorptive mechanism using computational intelligence, isotherm, kinetic, and thermodynamic analyses.

In this paper, folic acid-coated graphene oxide nanocomposite (FA-GO) is used as an adsorbent for the treatment of heavy metals including cadmium (Cd2+) and copper (Cu2+) ions. As such, graphene oxide (GO) is modified by folic acid (FA) to synthesize FA-GO nanocomposite and characterized by the atomic force microscopy (AFM), Fourier transform-infrared (FT-IR) spectrophotometry, scanning electron microscopy (SEM), and C/H/N elemental analyses. Also, computational intelligence tests are used to study the mechanism of the interaction of FA molecules with GO. Based on the results, FA molecules formed a strong π-π stacking, chemical, and hydrogen bond interactions with functional groups of GO. Main parameters including pH of the sample solution, amounts of adsorbent, and contact time are studied and optimized by the Response Surface Methodology Based on Central Composite Design (RSM-CCD). In this study, the equilibrium of adsorption is appraised by two (Langmuir and Freundlich and Temkin and D-R models) and three parameter (Sips, Toth, and Khan models) isotherms. Based on the two parameter evaluations, Langmuir and Freundlich models have high accuracy according to the R2 coefficient (more than 0.9) in experimental curve fittings of each pollutant adsorption. But, multilayer adsorption of each contaminant onto the FA-GO adsorbent (Freundlich equation) is demonstrated by three parameter isotherm analysis. Also, isotherm calculations express maximum computational adsorption capacities of 103.1 and 116.3 mg g-1 for Cd2+ and Cu2+ ions, correspondingly. Kinetic models are scrutinized and the outcomes depict the adsorption of both Cd2+ and Cu2+ followed by the pseudo-second-order equation. Meanwhile, the results of the geometric model illustrate that the variation of adsorption and desorption rates do not have any interfering during the adsorption process. Finally, thermodynamic studies show that the adsorption of Cu2+ and Cd2+ onto the FA-GO nanocomposite is an endothermic and spontaneous process.

Green material selection for sustainability: A hybrid MCDM approach.

Green material selection is a crucial step for the material industry to comprehensively improve material properties and promote sustainable development. However, because of the subjectivity and conflicting evaluation criteria in its process, green material selection, as a multi-criteria decision making (MCDM) problem, has been a widespread concern to the relevant experts. Thus, this study proposes a hybrid MCDM approach that combines decision making and evaluation laboratory (DEMATEL), analytical network process (ANP), grey relational analysis (GRA) and technique for order performance by similarity to ideal solution (TOPSIS) to select the optimal green material for sustainability based on the product's needs. A nonlinear programming model with constraints was proposed to obtain the integrated closeness index. Subsequently, an empirical application of rubbish bins was used to illustrate the proposed method. In addition, a sensitivity analysis and a comparison with existing methods were employed to validate the accuracy and stability of the obtained final results. We found that this method provides a more accurate and effective decision support tool for alternative evaluation or strategy selection.

Disassembly Sequence Optimization for Large-Scale Products With Multiresource Constraints Using Scatter Search and Petri Nets.

Disassembly modeling and planning are meaningful and important to the reuse, recovery, and recycling of obsolete and discarded products. However, the existing methods pay little or no attention to resources constraints, e.g., disassembly operators and tools. Thus a resulting plan when being executed may be ineffective in actual product disassembly. This paper proposes to model and optimize selective disassembly sequences subject to multiresource constraints to maximize disassembly profit. Moreover, two scatter search algorithms with different combination operators, namely one with precedence preserved crossover combination operator and another with path-relink combination operator, are designed to solve the proposed model. Their validity is shown by comparing them with the optimization results from well-known optimization software CPLEX for different cases. The experimental results illustrate the effectiveness of the proposed method.

An Accurate de novo Algorithm for Glycan Topology Determination from Mass Spectra.

Determining the glycan topology automatically from mass spectra represents a great challenge. Existing methods fall into approximate and exact ones. The former including greedy and heuristic ones can reduce the computational complexity, but suffer from information lost in the procedure of glycan interpretation. The latter including dynamic programming and exhaustive enumeration are much slower than the former. In the past years, nearly all emerging methods adopted a tree structure to represent a glycan. They share such problems as repetitive peak counting in reconstructing a candidate structure. Besides, tree-based glycan representation methods often have to give different computational formulas for binary and ternary glycans. We propose a new directed acyclic graph structure for glycan representation. Based on it, this work develops a de novo algorithm to accurately reconstruct the tree structure iteratively from mass spectra with logical constraints and some known biosynthesis rules, by a single computational formula. The experiments on multiple complex glycans extracted from human serum show that the proposed algorithm can achieve higher accuracy to determine a glycan topology than prior methods without increasing computational burden.