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. This approach developed a dissimilarity canonical variable analysis (CVA), namely, it integrated the dissimilarity analysis concept into CVA, enabling the description of incipient relationship among the process variables and quality variables. We also adopted the reconstruction-based contribution to separate the potential abnormal variable and form the candidate set. The transfer entropy method was used to identify the causal relationship between variables and establish the matrix and topology diagram of causal relationships for root cause diagnosis. We applied this unified framework to the practical operation data of tobacco strip processing from a tobacco factory. The results showed that, compared with traditional contribution plot of anomaly detection, the proposed approach cannot only accurately separate abnormal variables but also locate the position of the root cause. The dissimilarity CVA proposed in this study outperformed traditional CVA in terms of sensitiveness to faults. This method would provide theoretical support for the reliable abnormal detection and diagnosis in the tobacco production process.

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. Such model can predict the qualification of parts produced under different processing variables and further optimize processing variables of injection molding for minimal energy consumption and weight difference amongst parts in one cycle. Statistical assessment via F1-score and R2 was performed to evaluate the performance of the algorithm. In addition, to validate the effectiveness of our model, we conducted physical experiments to measure the energy profile and weight difference under varying parameter settings. Permutation-based mean square error reduction was adopted to specify the importance of parameters affecting energy consumption and quality of injection molded parts. Optimization results indicated that the processing parameters optimization could reduce ~ 8% energy consumption and ~ 2% weight difference compared with the average operation practices. Maximum speed and first-stage speed were identified as the dominating factors affecting quality performance and energy consumption, respectively. This study could contribute to the quality assurance of injection molded parts and facilitate energy efficient and sustainable plastic manufacturing.

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. This paper aims to critically and systematically evaluate the lifecycle environmental performance of remanufactured products compared with their new counterparts and to identify the key factors, strengths, and limitations in the assessment procedure. Faced with the noteworthy variations, we closely examined and harmonized the unit function, allocation approach, system boundary, impact assessment method, and the underlying assumptions in screened 20 papers regarding 11 types of products. The environmental indicators adopted in this study were global warming potential (GWP) and primary energy consumption (PEC). In terms of these two indicators, the environmental burdens of remanufactured products relative to newly manufactured alternatives were harmonized to the comparison ratios. With these harmonized samples, descriptive statistics were calculated using Monte Carlo Simulation to disclose the variations of comparison results and identify the general tendency. Results of this meta-study showed that remanufacturing could contribute to over 50% reduction for GWP when usage and end-of-life stages were excluded from the life cycle. The GWP and PEC of remanufactured mechanical products account for 28.5% and 25.9% of the new counterparts, respectively, on average. This meta-analysis of comparative LCAs on new and remanufactured products would advance the understanding of the environmental advantages of remanufacturing.

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. various processing time and uncertain processing routes). This paper built a metamorphosis CNT conjunct with coupled map lattice (CML) algorithm to predict the bottleneck shifting phenomenon in remanufacturing for the first time. The CNT was applied to the articulation of remanufacturing process, while the CML algorithm was devoted to calculating the dynamic indicator of the bottleneck. We took the value-added connecting rod as the research object to illustrate the availability of the proposed method. As validated by Arena simulation, the approach presented in this paper put forward is feasible to make an accurate prediction for shifting bottlenecks in a remanufacturing system.

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. It adopted the graphical evaluation and review technique (GERT) to convert remanufacturing process into an uncertain network, considering multivariant uncertainties instead of merely stochastic uncertainty in prior works. We provided a generic seven steps to implement this approach. The energy consumption and completion time of remanufacturing process were determined in conjunction with Mason's rule and chance-constrained programming. Connecting rod reprocessing was presented as a numerical example. Based on the GERT network, we conducted an Arena simulation to validate the feasibility and effectiveness of this approach. In addition, we adopted the concept of criticality index to conduct sensitivity analysis and examine the predominant factors affecting the concerned indicators. This study would enable remanufacturers to perform a quick prediction of energy use and makespan in remanufacturing process.

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. The implementation of both can reinforce each other, generating higher economic and environmental benefits than the sum of each occurrence alone. Another major finding from our model is that an additional remanufactured engine only displaces 0.42 (90% confidence interval from 0.32 to 0.47) of a new engine (comprised of new parts), mainly because the lower prices of remanufactured engines lead to greater consumption. This ratio is much lower than the 1:1 perfect displacement commonly assumed in life cycle assessment (LCA) studies. Overall, our study suggests that the subsidizing of engine remanufacture in China can help promote the industry, improve overall economic welfare, and contribute to environmental targets. Our study also contributes to the estimation of more realistic product displacement ratios in LCA.