Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location–routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network

Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax

In order to solve the optimization problem of the refined oil distribution system from the perspectives of low-carbon and environmental protection, this paper focuses on the characteristics of the secondary distribution of refined oil and combines it with the integrated optimization concept of refined oil distribution network, where a low-carbon inventory routing problem (LCIRP) model is constructed with the minimum total costs as the objective function on the basis of considering carbon emissions. An adaptive genetic algorithm combined with greedy algorithm is designed to solve the model, and an example is given to verify the effectiveness of the algorithm. Then, this paper solves the model with two parts by introducing a practical numerical example: in the first part, the LCIRP models with different carbon tax values are solved, which verifies the effectiveness of the model and proves that carbon tax policies can effectively reduce the carbon emissions in the secondary distribution network of refined oil; in the second part, the LCIRP models with the different maximum load capacity of oil tank trucks are solved, which provides the economic and environmentally friendly distribution schemes for refined oil distribution enterprises under the premise of carbon tax policies and load limitation. Finally, the emission reduction proposals that take into account both economic and environmental benefits are given respectively from the aspect of government environmental protection agencies and from the aspect of refined oil distribution enterprises

Optimization of Vehicle Routing Problem with Time Windows for Cold Chain Logistics Based on Carbon Tax

In order to reduce the cost pressure on cold-chain logistics brought by the carbon tax policy, this paper investigates optimization of Vehicle Routing Problem (VRP) with time windows for cold-chain logistics based on carbon tax in China. Then, a green and low-carbon cold chain logistics distribution route optimization model with minimum cost is constructed. Taking the lowest cost as the objective function, the total cost of distribution includes the following costs: the fixed costs which generate in distribution process of vehicle, transportation costs, damage costs, refrigeration costs, penalty costs, shortage costs and carbon emission costs. This paper further proposes a Cycle Evolutionary Genetic Algorithm (CEGA) to solve the model. Meanwhile, actual data are used with CEGA to carry out numerical experiments in order to discuss changes of distribution routes with different carbon emissions under different carbon taxes and their influence on the total distribution cost. The critical carbon tax value of carbon emissions and distribution cost is obtained through experimental analysis. The research results of this paper provide effective advice, which is not only for the government on carbon tax decision, but also for the logistics companies on controlling carbon emissions during distribution

Heterogeneous catalysts for CO2 hydrogenation to formic acid/formate: from nanoscale to single atom

Propelled by the vision of carbon-neutral energy systems, heterogeneous hydrogenation of CO2 to formic acid/formate, a liquid hydrogen carrier, has been intensively studied as a promising approach to realize renewable and decarbonized energy supply. In the present review, the state-of-the-art of heterogeneous catalysts for this process is comprehensively summarized. First, a brief description of the challenges associated with thermodynamics is provided. Major advancements on constructing efficient heterogeneous catalysts then constitute the main body of this review, mainly involving nanostructured and single atom catalysts based on noble metals. Special attention is paid to the relevant structure-activity correlations and mechanistic insights, which provide strong bases for rational catalyst design. Key factors related to catalytic activity are highlighted including metal dispersion, electron density, basic functionalities, and concerted catalysis of metal and basic sites. A summary and outlook is presented in the end. We believe that this review will inspire more novel research from the catalysis community to advance the design of innovative catalytic materials towards the ultimate sustainable energy sector with a closed carbon loop

Liquid crystal monomers in ventilation and air conditioning dust: Indoor characteristics, sources analysis and toxicity assessment

Indoor dust contaminated with liquid crystal monomers (LCMs) released from various commercial liquid crystal display (LCD) screens may pose environmental health risks to humans. This study aimed to investigate the occurrence of 64 LCMs in ventilation and air conditioning filters (VACF) dust, characterize their composition profiles, potential sources, and associations with indoor characteristics, and assess their in vitro toxicity using the human lung bronchial epithelial cells (BEAS-2B). A total of 31 LCMs with concentrations (ΣLCMs) ranging from 43.7 ng/g to 448 ng/g were detected in the collected VACF dust. Additional analysis revealed the potential interactions between indoor environmental conditions and human exposure risks associated with the detected LCMs in VACF dust. The service area and working time of the ventilation and air conditioning system, and the number of indoor LCD screens were positively correlated with the fluorinated ΣLCMs in VACF dust (r = 0.355 ∼ 0.511, p  0.05), suggesting different environmental behavior and fates of fluorinated and non-fluorinated LCMs in the indoor environment. Four main indoor sources of LCMs (i.e., computer (37.1%), television (28.3%), Brand A smartphone (21.2%) and Brand S smartphone (13.4%)) were identified by positive matrix factorization-multiple linear regression (PMF-MLR). Exposure to 14 relatively frequently detected LCMs, individually and in the mixture, induced significant oxidative stress in BEAS-2B cells. Among them, non-fluorinated LCMs, specifically 3cH2B and MeP3bcH, caused dominant decreased cell viability. This study provides new insights into the indoor LCMs pollution and the associated potential health risks due to the daily use of electronic devices

Composition prediction of pore solution in hardened concrete materials based on machine learning

The pore solution composition (OH−, Na+, K+, Ca2+ and SO42-, S2O32-, S2- concentrations) of hardened concrete materials, including binary systems of PC mixed with a single SCM and with two SCMs, was investigated. Based on database comprising more than 400 entries with more than 80 parameters, machine learning (ML) is applied to predict ion concentrations. Catboost model is the optimal model. The concentrations of OH−, Na+, K+ were predicted with high accuracy (R2 of 0.92–0.95). The prediction accuracy of S is low, could also reaches a R2 of 0.79. But the prediction accuracy of linear regression model is very low, with R2 of 0.18–0.7. PC_MgO, PC_Na2O, PC_K2O and SCM_SiO2 rank high in the characteristic importance analysis for predicting the concentration of OH−, Na+, K+. Compared with the classical pore solution prediction methods (Taylor's and NIST algorithm), the ML model is more accurate. Due to the use of more data and kinds of methods, the ML prediction results in this paper are also better than Cristhiana's ML model. These ML models can be used to predict pore solution of more than 28 d old normal PC concrete with silica fume, fly ash, slag, limestone or quartz powder, but PC or SCM with high phosphorus oxide is not suitable