scholarly journals A Data-Driven Packaging Efficiency Optimization Method for a Low Carbon System in Agri-Products Cold Chain

2022 ◽  
Vol 14 (2) ◽  
pp. 858
Author(s):  
Jingjie Wang ◽  
Xiaoshuan Zhang ◽  
Xiang Wang ◽  
Hongxing Huang ◽  
Jinyou Hu ◽  
...  
Keyword(s):  
Optimization Method ◽  
Evaluation Framework ◽  
Evaluation Index System ◽  
Data Driven ◽  
Cold Chain ◽  
Efficiency Evaluation ◽  
Low Carbon ◽  
Process Data ◽  
Packaging Efficiency ◽  
Carbon System

The of monitoring the Internet of Things (IoT) in the cold chain allows process data, including packaging data, to be more easily accessible. Proper optimization modelling is the core driving force towards the green and low-carbon operation of cold chain logistics, laying the necessary foundation for the development of a data-driven modelling system. Since efficient packaging is necessary for loss control in the cold chain, its final efficiency during circulation is important for realizing continuous loss prevention and efficient supply. Thus, it is urgent to determine how to utilize these continuously acquired data and how to formulate a more accurate packaging efficiency control methodology in the agri-products cold chain. Through continuous monitoring, we examined the feasibility of this topic by focusing on the concept of data-driven evaluation modelling and the dynamic formation mechanism of comprehensive packaging efficiency in cold chain logistics. The packaging efficiency in the table grape cold chain was used as an example to evaluate the comprehensive efficiency evaluation index system and data-driven evaluation framework proposed in this paper. Our results indicate that the established methodology can adapt to the continuity of comprehensive packaging efficiency, also reflecting the comprehensive efficiency evaluation of the packaging for different times and distances. Through the evaluation of our results, the differences and the dynamic processes between different final packaging efficiencies at different moments are effectively displayed. Thus, the continuous improvement of a low-carbon system in cold chain logistics could be realized.

scholarly journals Environmental Efficiency Evaluation of China’s Power Industry Based on the Two-Stage Network Slack-Based Measure Model

2021 ◽  
Vol 18 (23) ◽  
pp. 12650
Author(s):  
Wei Wei ◽  
Shuangying Ding ◽  
Silin Zheng ◽  
Jingjing Ma ◽  
Tong Niu ◽  
...  
Keyword(s):  
Power Generation ◽  
Power Industry ◽  
Environmental Efficiency ◽  
Evaluation Index System ◽  
Efficiency Evaluation ◽  
Low Carbon ◽  
Generation Efficiency ◽  
Two Stage ◽  
Transmission And Distribution ◽  
Distribution Efficiency

How to achieve the continuous improvement of the environmental performance level of the power industry within the requirements of clean and low-carbon energy development is the fundamental requirement and inevitable choice for the construction of ecological civilization and sustainable development. From the perspective of environmental protection, based on the Data Envelopment Analysis (DEA) method and the internal mechanism of power system production and supply, the power industry environmental efficiency evaluation index system was constructed, and the two-stage Network Slack-based Measure (NSBM) model considering undesired output was used to calculate China’s 30 provinces and municipalities from 1998 to 2019. The environmental efficiency is divided into two links: power generation efficiency and transmission and distribution efficiency. The study found that, within the research interval, the overall environmental efficiency of China’s 30 provinces is low, and the differences between provinces and cities are large, but they have gradually developed in a better direction after 2015. The power generation efficiency of the first link in most provinces and municipalities is higher than the transmission and distribution efficiency of the second link, and the low transmission and distribution efficiency is an important reason for the low comprehensive level of environmental efficiency. The overall evolution trend of environmental efficiency in the six regions of China is roughly the same, but the regional differences are obvious, showing a trend of “high in the southeast and low in the northwest”. The economic and natural resource differences in different provinces and cities in each region have led to varying degrees of redundancy in five aspects, including investment in power assets, installed power generation capacity, and length of transmission lines, which seriously affect the environmental efficiency of the power industry. This research attempts to open the “black box” of the environmental efficiency conversion process of the power industry, which can provide directions and strategic suggestions for the improvement of the efficiency of the power industry in China.

scholarly journals Identification of Granule Growth Regimes in High Shear Wet Granulation Processes Using a Physics-Constrained Neural Network

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 737
Author(s):  
Chaitanya Sampat ◽  
Rohit Ramachandran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Data Driven ◽  
Wet Granulation ◽  
Manufacturing Processes ◽  
High Shear ◽  
Process Data ◽  
Physical Constraints ◽  
Granule Growth

The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulation process. The physics-constrained neural network (PCNN) was better at predicting granule growth regimes when compared to other neural networks with no physical constraints. When input data that violated physics-based boundaries was provided, the PCNN identified these points more accurately compared to other non-physics constrained neural networks, with an error of <1%. A sensitivity analysis of the PCNN to the input variables was also performed to understand individual effects on the final outputs.

Research on Comprehensive Post-Evaluation of Electric Transmission Project Based on Fuzzy AHP Analysis Method

2013 ◽  
Vol 415 ◽  
pp. 280-286 ◽  
Author(s):  
Li Xin Zheng
Keyword(s):  
Power Transmission ◽  
Fuzzy Ahp ◽  
Investment Decision ◽  
Power Grid ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Scientific Management ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Post Evaluation

Post-evaluation of power transmission project will help improve the scientific management level of the construction implementation and operation process, the investment decision-making and returns, and ensure the safe operation of power grid. Based on the theories and methods to establish the evaluation index, considering the low-carbon economy requirements on power grid construction, this article established a post-evaluation index system from the perspective of implementation process, effectiveness and efficiency, social and environmental impact, and sustainable development. According to the content of post-evaluation of power transmission project, it built a fuzzy AHP comprehensive post-evaluation model and took power transmission project as an example to analyze the application of the model.

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