A service-oriented energy assessment system based on BPMN and machine learning

Increasing energy cost and environmental problems push forward research on energy saving and emission reduction strategy in the manufacturing industry. Energy assessment of machining, as the basis for energy saving and emission reduction, plays an irreplaceable role in engineering service and maintenance for manufacturing enterprises. Due to the complex energy nature and relationships between machine tools, machining parts, and machining processes, there is still a lack of practical energy evaluation methods and tools for manufacturing enterprises. To fill this gap, a serviced-oriented energy assessment system is designed and developed to assist managers in clarifying the energy consumption of machining in this paper. Firstly, the operational requirements of the serviced-oriented energy assessment system are analyzed from the perspective of enterprises. Then, based on the establishment of system architecture, three key technologies, namely data integration, process integration, and energy evaluation, are studied in this paper. In this section, the energy characteristics of machine tools and the energy relationships are studied through the working states of machine tools, machining features of parts and process activities of processes, and the relational database, BPMN 2.0 specification, and machine learning approach are employed to implement the above function respectively. Finally, a case study of machine tool center stand base machining in a manufacturing enterprise was applied to verify the effectiveness and practicality of the proposed approach and system

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups. Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial.

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial (vol 26, 46, 2022)

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model

The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods

A hierarchical integration scheduling method for flexible job shop with green lot splitting

The integration of green scheduling and lot splitting scheduling is indispensable for ensuring the coordinated optimization of economic and environmental benefits in flexible job-shop scheduling (FJS). However, this integration involves not only the indicator of greenness and economy but also the process of production planning and scheduling, which is substantially complicated. To this end, a hierarchical integrated scheduling method is proposed by comprehensively considering the multilevel organizational structure and task configuration characteristics of flexible job-shop, as well as the differences in objectives on different scheduling levels: workshop level, process unit level, machine tool level. On the workshop level, a lot splitting model is presented to obtain the optimal processing task set for each production cycle with the minimum expected cost (startup cost, tardiness cost, and holding cost). On the process unit level, a task allocation model is given to allocate the optimal workload for each machine tool with the minimum processing energy consumption and maximum machine load. On the machine tool level, an operation sequencing model is established to obtain the optimal processing sequence for each machine tool with the minimum standby energy consumption and makespan. According to the solving characteristics of the hierarchical models, a multi-objective algorithm is applied. Finally, a case study is demonstrated to validate the proposed method

A Decision-Making Model for Remanufacturing Facility Location in Underdeveloped Countries: A Capacitated Facility Location Problem Approach

Underdeveloped countries are gradually opening remanufacturing facilities to recover end-of-life products (EOL). Locating these facilities in underdeveloped countries is quite challenging because many factors related to the environment, economics, and ethics have to be considered. This paper proposes a decision-making model for locating remanufacturing facilities, a critical factor in implementing remanufacturing in underdeveloped countries. Our principal objective is to obtain the capacity, number, and geographical locations for newly established remanufacturing facilities using a Capacitated Facility Location Problem (CFLP) approach. The mathematical model helps us find the number of facilities that will need to be opened to fully recover the EOL products and the total cost during the entire process. A case study on the establishment of SEVALO Remanufacturing Machinery Co., Ltd. in Cameroon is used to demonstrate the CFLP approach. The results and analyses show that the successful establishment of SEVALO in Cameroon will significantly help to reduce the quantity of construction machinery parts dumped into the environment

Multi-Objective Parameter Optimization Dynamic Model of Grinding Processes for Promoting Low-Carbon and Low-Cost Production

Grinding is widely used in mechanical manufacturing to obtain both precision and part requirements. In order to achieve carbon efficiency improvement and save costs, carbon emission and processing cost models of the grinding process are established in this study. In the modeling process, a speed-change-based adjustment function was introduced to dynamically derive the change of the target model. The carbon emission model was derived from the grinding force using regression. Considering the constraints of machine tool equipment performance and processing quality requirements, the grinding wheel’s linear velocity, cutting feed rate, and the rotation speed of the workpiece were selected as the optimization variables, and the improved NSGA-II algorithm was applied to solve the optimization model. Finally, fuzzy matter element analysis was used to evaluate the most optimal processing plan

Timing Decision for Active Remanufacturing Based on 3E Analysis of Product Life Cycle

Active remanufacturing is an important technique that is used to reduce the uncertainty of the quality of remanufactured cores. However, the implementation of active remanufacturing too early or late will lead to a reduction in economic benefits and an increase in environmental impact during the whole life cycle of the product. To this end, an active-remanufacturing-timing decision method is proposed based on an economic, energy and environmental (3E) analysis of product life cycle. In this method, the quantitative function of the cost, energy consumption and environmental emissions of used products in the manufacturing stage, service stage, and remanufacturing stage are firstly constructed based on life-cycle assessment (LCA) and life-cycle cost (LCC). Then, a multi-objective optimization method and the particle swarm algorithm are utilized to obtain active-remanufacturing timing with the optimal economic and environmental benefits of remanufacturing. Finally, a case study on remanufacturing on used engines is demonstrated to validate the proposed method