Energy Consumption Prediction of Injection Molding Process Based on Rolling Learning Informer Model.

Accurate energy consumption prediction in the injection molding process is crucial for optimizing energy efficiency in polymer processing. Traditional parameter optimization methods face challenges in achieving optimal energy prediction due to complex energy transmission. In this study, a data-driven approach based on the Rolling Learning Informer model is proposed to enhance the accuracy and adaptability of energy consumption forecasting.

Toward More Realistic Estimates of Product Displacement in Life Cycle Assessment.

Life-cycle assessment (LCA) is one of the most widely applied methods for sustainability assessment. A main application of LCA is to compare alternative products to identify and promote those that are more environmentally friendly. Such comparative LCA studies often rest on, explicitly or implicitly, an idealized assumption, namely, 1:1 displacement between functionally equivalent products.

Dynamic monitoring and anomaly tracing of the quality in tobacco strip processing based on improved canonical variable analysis and transfer entropy.

Multivariate statistical monitoring methods are proven to be effective for the dynamic tobacco strip manufacturing process. However, the traditional methods are not sensitive enough to small faults and the practical tobacco processing monitoring requires further root cause of quality issues. In this regard, this study proposed a unified framework of detection-identification-tracing.

Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding.

The high energy intensity and rigorous quality demand of injection molding have received significant interest under the background of the soaring production of global plastic industry. As multiple parts can be produced in a multi-cavity mold during one operation cycle, the weight differences of these parts have been demonstrated to reflect their quality performance. In this regard, this study incorporated this fact and developed a generative machine learning-based multi-objective optimization model.

Environmental benefits of remanufacturing mechanical products: a harmonized meta-analysis of comparative life cycle assessment studies.

Remanufactured mechanical products with high-added value are generally claimed to gain environmental benefits. These claims were made based on different products and assessment methodologies. The variability of life cycle assessment (LCA) results precludes a meaningful comparison across products and studies.

A model to predict bottlenecks over time in a remanufacturing system under uncertainty.

Bottleneck shifting prediction has been widely applied to the remanufacturing system for throughput improvement, and it would directly influence the general presentation of the remanufacturing system. However, predicting dynamic bottlenecks of remanufacturing systems is complicated due to the disturbed environment (e.g.

An energy and time prediction model for remanufacturing process using graphical evaluation and review technique (GERT) with multivariant uncertainties.

The rising energy price and stringent energy efficiency-related legislations encourage decision makers to concern more about energy efficiency in current manufacturing competition. In this regard, a quick and accurate prediction of the energy consumption and makespan in the manufacturing process has been a prerequisite for energy optimization. Given the various types of uncertainties in the remanufacturing system such as stochastic, fuzzy, and grey factors, the present study developed a prediction model that forecasts the energy consumption, completion time, and probability of processing routes.

Environmental Benefits of Engine Remanufacture in China's Circular Economy Development.

China has recently implemented broad strategies aimed at achieving a circular economy by providing subsidies for the remanufacture industry and setting a target of 15% increase in energy efficiency in industrial production across sectors, among other strategies. Here, we examine the environmental implications of these policies in the context of engine remanufacture, using an environmental computable general equilibrium (CGE) model. Results indicate that both the subsidy policy and energy efficiency improvement target can contribute to economic growth and emission reductions, but the subsidy policy is estimated to have far greater impacts.