Production Scenarios Research Articles

A [formula omitted]-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources

With the widespread adoption of intelligent transportation equipment such as AGVs in the manufacturing field, the flexible job shop scheduling considering limited transportation resources has increasingly attracted attention. However, current research does not consider various dynamic disturbances in real production scenarios, resulting in lower executability of scheduling solutions. To solve this problem, a dynamic flexible job shop scheduling model with limited transportation resources is established, aiming to minimize makespan and total energy consumption. Considering three types of disturbances: job cancellation, machine breakdown, and AGV breakdown, the corresponding event-driven rescheduling strategy is proposed, and a rescheduling instability index is designed to measure the performance of the rescheduling strategy. A Q-Learning-based NSGA-II algorithm (QNSGA-II) is proposed. By learning the feedback historical search experience, it adaptively selects the appropriate neighborhood structures for local search; and a hybrid initialization strategy tailored to the problem characteristics is designed to improve the optimization performance of the algorithm. Through simulation experiments, the effectiveness of the rescheduling strategies and the superiority of the QNSGA-II algorithm in solving such problems are validated.

PRESENT SCENARIO OF LIVESTOCK BREEDING AND MANAGEMENT PRACTICES IN THE NORTHERN PART OF BANGLADESH

<p><strong>Background: </strong>Bangladesh has a rich heritage of livestock rearing. The management and breeding practices differ across the country. <strong>Objectives:</strong> To study the present scenario of breeding and management practices of livestock at northern Teesta river basin based Lalmonirhat district in Bangladesh. <strong>Methodology: </strong>Total 100 farmers were selected using random sampling technique from 5 upazilas of Lalmonirhat district during July to December, 2022. Data were analyzed using SPSS 23.0 statistical package. <strong>Results: </strong>Results showed that most of the farmers (65%) at the northern part of Bangladesh were middle-aged, 41% of them completed secondary education, and their occupation was mainly agriculture (55%). About 37.78% of respondents supplied roadside grass as roughage to their livestock. Maximum number of respondents (60.24%) supplied hand mixed feed as a source of concentrate whereas 39.76% of respondents used commercial feed. 96% of farmers practiced artificial insemination to inseminate their cows and heifers while in goat nearly all respondent farmers (98.41%) practiced natural mating. Among the breeding companies, 41.41% farmers preferred semen from the Bangladesh Rural Advancement Committee (BRAC), followed by Advanced Chemical Industries (ACI) (30.30%), Government (Department of Livestock Services (DLS) (22.22%), and others (6.07%). About 41.76% respondents used 50% Sahiwal - 50% local genotype bull to breed the cows whereas 23.08% respondents used 100% Sahiwal, 18.68% used 75% Holstein Friesian-25% local, 5.49% used 100% Holstein Friesian, 3.30% used 50% Holstein Friesian-50% Local and only 3.30% used 87.5% Holstein Friesian-12.5% Local genotype bull semen, respectively.<strong> </strong>On the other hand, most of the farmers (98.41%) chosen Black Bengal breeding buck during breeding and remaining used crossbred (Black Bengal Goat- Jamunapari) to breed their does. The actual price of bull semen from Govt. (DLS) was 30 Bangladeshi Taka (BDT) on average for all the breeds whereas, the price ranged 110-200 BDT depending on cattle breeds and bull/bucks Identification (ID) in different private enterprises. Farmers had to pay on an average<strong> </strong>181.18 BDT for DLS originated semen in addition to that BRAC Artificial Insemination (AI) workers took an average of 425.61 BDT from farmers to inseminate their cows. The average milk yield/d was 1.82±0.14, 3.35±0.40, and 2.74±0.27 liters for local, HF crossbred and Sahiwal crossbred respectively in that region. The prevalence of repeat breeding incidences was 13.74%, 14.21%, and 15.17%, for Local, Sahiwal crossbred, and Holstein Friesian crossbred genotypes respectively. The highest incidence of disease was found as Lumpy Skin Disease (14.67%) followed by Foot and Mouth Disease (FMD) (11.98%) and other parasitic and metabolic diseases. In the study area, it was found that maximum number of the respondents (95%) faced excessive feed price problem along with some other problems. <strong>Implications: </strong>Government intervention is necessary to improve the situation of livestock production in the country. <strong>Conclusion:</strong> This study showed the overall scenario of livestock production, breeding, and management in the northern part of Bangladesh which could be helpful for the govt., Non-governmental organizations (NGO’s) and policymakers to take realistic steps for the improvement of livestock production in the northern part of Bangladesh.</p>

Live fitting of process data within digital twins of manufacturing to use simulation and optimisation

ABSTRACT In production scenarios, uncertainty in production times, and scrap rates is common. Uncertainty can be described by stochastic models that need continuous updates due to changing conditions. This paper models probability distributions and estimates their parameters from real-world data on processing times and scrap rates. It uses live fitting to identify timely changes in the data sets, comparing different distributions. The fitting and live fitting approaches are applied to data from a real production system to compare the goodness of fit for different distributions and to demonstrate the reliability of the reaction to changes in the input data. Data and probability estimations are categorised, and a concept combining simulation and optimisation models is developed to optimise scheduling. The vision of the research presented in this paper is an online approach that determines quasi-optimal schedules for production systems based on current data from the system and its environment.

Integrating solar-driven biomass gasification and PV-electrolysis for sustainable fuel production: Thermodynamic performance, economic assessment, and CO2 emission analysis

Biomass gasification provides a promising pathway for sustainable fuel production. Nevertheless, its development is hindered by lower efficiencies in biomass–to–fuel energy conversion and carbon utilization, along with restricted scale due to economic collection radius. To tackle these challenges, a solar–biomass hybrid gasification system is proposed for sustainable fuel production. The system employs parabolic trough and tower concentrators to generate solar heat, driving the biomass pyrolysis and pyrolysis products gasification reactions to produce bio–solar syngas. The high–density bio–oil and char produced in distributed pyrolysis units can be economically transported to the centralized gasification plant. Moreover, PV–electrolysis water is utilized to generate H2 and O2, which are used to adjust syngas composition and substitute for air separation O2, respectively. Consequently, the energy consumption for water–gas shift, CO2 separation, and air separation units can be reduced. Thermodynamic, economic, and CO2 emission performance are analyzed for a typical case of methanol production using corn residue. The total energy conversion efficiency of the hybrid system can reach 65.01 %, representing an improvement of 6.57 % compared to the conventional biomass gasification system. The fuel production cost is estimated to be $354.1 per ton, falling within the market price range. Additionally, the greenhouse gas emissions to produce unit kg methanol can be reduced by 0.63 kg CO2eq. Furthermore, we analyzed the performance of six representative fuel production scenarios involving different types of biomass feedstocks. Finally, the limitations of this work and the feasibility of its practical applications are discussed. This study offers an innovative approach to bio-solar fuel production, promoting the development of circular bio-economy and the application of sustainable fuel.

Implementation of a Numerically Stable Algorithm for Capillary Hysteresis in Gas Hydrate Deposits.

We develop a numerically stable algorithm of intrinsic capillary hysteresis for numerical simulation of gas hydrate deposits where cyclic drainage and imbibition processes occur. The algorithm is motivated by the elastoplastic return mapping, and it is an extension of the recently developed algorithm of two-phase immiscible flow, which provides numerical stability with the fully implicit method. We consider the effective gas and aqueous saturations normalized by total fluid phase saturation implicitly affected by the dynamic formation and dissociation of hydrates. Specifically, gas saturation is additively decomposed into the reversible and irreversible parts, and the algorithm computes the reversible and irreversible parts dynamically during the evolution of gas saturation. We perform numerical tests, including a field-scale case, by implementing the code of the capillary hysteresis in a gas hydrate flow simulator. We find that the developed algorithm is stable and robust for repeated drainage and imbibition processes in gas hydrate systems. Since cyclic depressurization is one of the promising production scenarios for gas production from marine gas hydrate deposits, the developed algorithm and code will provide robust and high-fidelity simulation in the forward simulation of multiphase flow.

Surface defect detection of strip steel based on GT-CutMix augmentation algorithm and improved DSSD model

Abstract Nowadays, defect detection technology based on deep learning continuously increases the surface quality requirements of hot-rolled strip steel. However, due to limitations in industrial production, defect datasets often suffer from insufficient training samples and imbalanced categories. This paper proposes effective solutions, namely the GT-CutMix offline data augmentation algorithm and lightweight small sample defect detection models. The GT-CutMix augmentation algorithm significantly improves defect utilization by accurately sampling defect locations and integrating them into the original data set. We design the S-DSSD defect detection model by constructing a lightweight SI-MobileNet to replace the ResNet101 backbone of the Deconvolutional Single Shot Detector (DSSD) network. This can reduce the resource parameters and consumption. At the same time, it can speed up training and inference. To further improve the detection accuracy, we integrate the Pyramid Split Attention (PSA) attention mechanism into the prediction module of DSSD and construct the SA-DSSD model. Under the GT-CutMix augmentation algorithm, the mAP of S-DSSD and SA-DSSD models on X-SDD dataset are 76.83% and 78.63%, respectively. Meanwhile, the corresponding detection speeds are 45 FPS and 40 FPS, respectively. In addition, on the NEU-DET cross-dataset experiment, the mAP of the SA-DSSD model reaches 74.88%. Our methods are highly effective and generalized for small sample defect detection, which can provide selective solutions for specific needs such as high speed and precision in different industrial production scenarios.

Life cycle assessment of roll-to-roll produced chemical vapor deposition graphene transparent electrodes towards copper foil recycling

Graphene transparent electrode (GTE) has been attracting much attention due to fascinating physical properties. However, the extensive deployment of copper foil within GTE production has imparted substantial environmental burden. This paper is a cradle-to-gate life cycle assessment (LCA) study to investigate the environmental impacts of roll-to-roll produced chemical vapor deposition (CVD) GTE and the environmental potential of recycling copper foil for cleaner production. Four production scenarios are developed to promote the lab-to-fab progress, including lab scenario, industry baseline scenario, industry recycling scenario and microwave plasma chemical CVD scenario. The functional unit is set as 1 m2 of the GTE production and the life cycle inventories of different scenarios are explored. Results show that the copper foil is a major contributor in baseline scenario in the category of primary energy consumption and global warming. The impacts of GTE production in industry recycling scenario vary from 0.01 to 0.18 of the values in industry baseline scenario. Therefore, copper foil recycling shows environmental potential for GTE production. If all building integrated photovoltaics transition to employing perovskite solar cells with GTE produced in copper recycling scenario, the potential reduction in CO2 emissions is estimated at 141.2 million kilograms per year. The findings serve as a roadmap for the industry, highlighting key areas where improvements can be made to upscale production while minimizing environmental impact. This paper provides insights into the major environmental contributors in the GTE production, guiding the upscaling routes for cleaner GTE production in the future.

P2M systems based on proton-conducting solid oxide cells: Future prospects and costs of renewable methanol production

As consequence of the transition towards sustainable energy sources, the future production of liquid energy carriers (e.g. methanol) via H2 supply pathways utilizing water electrolyzers (power-to-liquid) will most likely be based on fluctuating grid electricity or islanded renewable inputs. As a result, these production processes are subject to fluctuating operating conditions and varying production capacities, ultimately leading to uncertainties with respect to their process economics and profitability. Therefore, the impact of different electricity supply-side scenarios (static grid and intermittent grid or renewable supply) on the production economics of power-to-liquid processes needs to be assessed thoroughly for the upcoming decades. Methanol is considered as an essential base chemical which is widely known for its versatility and broad potential use contexts in future chemical industries and energy storage applications. Methanol production pathways powered by renewable electricity sources, also known as power-to-methanol processes, are characterized by low specific life-cycle emissions and are therefore of paramount interest. One possible renewable process chain features proton-conducting high temperature electrolyzers combined with a direct hydrogenation of CO2. In this paper, a techno-economic forecast study of this process chain is presented and specific production costs of renewable methanol under different electricity supply scenarios are determined and discussed for the years 2030 and 2050. The studies showed that flexible grid-supported scenarios through direct spot-market participation and renewable scenarios based on wind onshore production enable the largest production cost reduction potential in the upcoming decades. Minimum production costs of 740 € t−1MeOH (2030) and 415 € t−1MeOH (2050) are determined for a flexible operation of the system with spot-market participation, benefitting from times of low or even negative electricity prices. Among the renewable production scenarios, islanded power-to-methanol systems coupled to wind onshore plants were identified as the most beneficial configuration with ascertained production costs as low as 820 € t−1MeOH and 353 € t−1MeOH by 2030 and 2050, respectively.

A DRL-Based Reactive Scheduling Policy for Flexible Job Shops With Random Job Arrivals

In real-life production systems, arrivals of jobs are usually unpredictable, which makes it necessary to develop solid reactive scheduling policies to meet delivery requirements. Deep reinforcement learning (DRL) based scheduling methods are capable of quickly responding to dynamic events by learning from the training data. However, most of policy networks in DRL algorithms are trained to choose priority dispatching rules (PDR), thus, to some extent, the efficiency of obtained scheduling plans is limited by the performance of PDRs. This paper investigates a dynamic flexible job shop scheduling problem with random job arrivals for the total tardiness minimization. A DRL-based reactive scheduling method, proximal policy optimization with attention-based policy network (PPO-APN), is proposed to make real-time decisions for the dynamic scheduling environment, where the attention-based policy network (APN) is able to directly select pending jobs distinguished from the action space that consists of PDRs. Additionally, a global/local reward function (GLRF) is designed to address the reward sparsity issue during training processes. The proposed PPO-APN is tested on randomly generated instances with different production configurations, and is compared with frequently-used PDRs and DRL-based methods. Numerical experimental results indicate that APN and GLRF components significantly improve the training efficiency, and the PPO-APN shows better overall performance compared with other methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by a typical production scenario in discrete manufacturing systems, where orders randomly arrive at the shop floor and require to be scheduled in a short time to ensure the on-time delivery. Previous research work tends to apply DRL algorithms to choose suitable dispatching rules for the ease of implementation. Nevertheless, the jobs that can be selected by dispatching rules are rather limited, thus many possible high-quality scheduling plans are ignored. This work first sorts all the unscheduled jobs by a heuristic algorithm, and puts some of top-ranked jobs to a pool. When a machine becomes available, it will directly choose a job from the pool as the next processing task. The job selection policy is represented by a novel attention-based network, and is trained by a powerful DRL algorithm. The aforementioned process is repeatedly executed in a simulation environment to collect the training data. Therefore, after being trained for a certain period of time, the policy will become smarter and can be applied to make right decisions in real-time. The proposed reactive scheduling method has been proved to be more efficient than dispatching rules and DRL-based approaches, and is effective in the production scheduling for a wide variety of discrete manufacturing scenarios, such as automobile and electronics industries. Moreover, the proposed method can be further extended to address dynamic scheduling problems with some production characteristics via adding constraints for the job selection or re-defining calculations for the completion time of operations accordingly.

인공발 제품의 사용적합성 평가 지침 개발 및 시험적용

Artificial foot is an assistive device worn by calf or thigh amputees to enable walking. Orthotic prosthesis including artificial foot are internationally classified as medical devices, but in Korea, they are still classified as assistive devices for the disabled rather than medical devices. All medical devices under the the Ministry of Food and Drug Safety regulation are required to conform to the international standard IEC 62366-1 since 2021. However, prosthetic orthotic products such as artificial foot are not classified as medical devices, so their usability test requirement has not yet been mandated. In this study, four prosthetic orthosis experts formed an advisory committee and applied IEC 62366-1, an international standard for medical device usability evaluation, to develop the usage scenarios for artificial foot products, in indoor and outdoor environments. After selecting 16 tasks in usage environments, we developed guidelines consisting of 52 questionnaires to determine the product's usability, including safety, effectiveness, and satisfaction. Three lower-leg amputees participated in the test application and responded to the survey of the guidelines for a prosthetic foot product while performing 16 tasks. this product received an average score of 3.42 out of 5, and the usability evaluation score was lower on unpaved roads and slippery tiles than on flat surfaces. It is expected that the experience in developing user suitability evaluation guidelines for artificial feet developed in this study can be usefully applied to the development of usability evaluation guidelines for other prosthetic and orthotic products.

PREDICTING RICE SELF-SUFFICIENCY IN SIERRA LEONE USING ARIMA MODEL

Rice is the staple food in Sierra Leone, with the annual consumption increasing faster than the yearly production. As a result, the county is yet to be self-sufficient in rice production, resulting in a very high annual import bill. This paper aims to predict Sierra Leone's self-sufficiency in rice until 2030. The time series modeling approach (Box Jenkins' ARIMA model) was used to forecast rice production, consumption, and finally, rice self-sufficiency in Sierra Leone. The predicted results of the study showed that the self-sufficiency rate of rice will fluctuate between 63% and 65% in the forecasted period of 2021 and 2024 and will then remain stable (constant) at 63% until 2030. Therefore, as shown from the study results, Sierra Leone will continue to import about 37% of rice annually at the current production scenario until 2030. This study's findings showcased that although the country has formulated some viable policies to boost rice production, more effort is needed to ensure self-sufficiency. However, any policy to increase rice production should strive to improve small-scale rice producers in line with their agronomic practices.

Land consolidation of dryland-to-paddy conversion in China: Analysis of spatial patterns, impacts on cropland ecosystem services, and adaptive management strategies

Irrigation agriculture has become integral to global food production because it drastically improves productivity. Land use pattern conversion significantly influences cropland water use, the soil environment, management practices, and ecosystem services (ESs). Studies on adaptive management strategies of land consolidation of dryland-to-paddy conversion (LC-DtPC) and their effects on ESs are scarce. Therefore, this study analyzed the evolutionary spatial pattern characteristics of LC-DtPC in China over the past 20 years, assessed the overall ESs gains or losses in the LC-DtPC areas, simulated the spatial patterns of LC-DtPC under different scenarios, and explored the adaptive management strategies of LC-DtPC. The results revealed that in the past 20 years, the LC-DtPC area reached 129,700 km2, accounting for 7.31% of the cultivated land area in 2020, was concentrated in China's southern and eastern regions, especially the Northeast Plain, Sichuan Basin, and surrounding areas. In the LC-DtPC zones, there were significant tradeoffs and synergistic relationships between the ESs. The food supply, water yield, and soil conservation services were promoted, while the carbon sequestration services were inhibited. Specifically, the former increased by 3787.55 × 104 t, 1329.55 × 104 m3, and 141.69 × 104 t respectively, while the latter decreased by 814.39 × 104 t. While the simulated spatial patterns of LC-DtPC varied under different scenarios, they were always concentrated in China's northeastern and southern regions. The balanced development scenario of agricultural production and ecological protection has the best long-term ecological effect, with differentiated adaptive management strategies for LC-DtPC (planning, implementing, monitoring, and evaluating). This study will fill a gap in ecosystem services of LC-DtPC, the results are instrumental in the sustainable utilization of cropland resources and regional sustainable development.

Multiphase flow and geomechanical behavior induced by gas production from silty-clay hydrate reservoirs through different horizontal well scenarios

Accurately predicting the coupled response behaviors during production is essential for the exploitation of gas hydrate. However, the multiphase flow and geomechanical analysis for gas production through horizontal wells in fine-grained sediments are few reported. In this study, a coupled thermal–hydraulic-mechanical (THM) model is carried out to assess the production response behaviors under different schemes. The simulation results indicate that the cumulative gas production for dual horizontal wells by depressurization assisted with hot water injection (Winj) is about 4.0 times and 2.46 times as much as that of single horizontal one and dual horizontal wells without Winj during the short-term production, respectively. However, the corresponding multiples in gas production become 2.63 and 1.40 times for the long-term production. The seafloor uplift for these three production scenarios is similar and approximately 0.06 m within 30 days. However, the ultimate seabed subsidence are 1.15 m, 1.56 m and 2.59 m, respectively. The Z-displacement undergoes approximately linear reduction with depth, while the X-displacement exhibits a gradient distribution except in the near-wellbore area. By contrast, dual horizontal wells under the hybrid production condition have the highest gas productivity and the minimal instability risk. Furthermore, the modified Fetkovich formula can effectively perform the inflow dynamic analysis of horizontal well systems by depressurization. These findings provide a reference for determining production strategies involving horizontal wells in silty clay reservoirs.

Adoption of precision livestock farming technologies has the potential to mitigate greenhouse gas emissions from beef production

To meet the objectives of the Paris Agreement, which aims to limit the increase in global temperature to 1.5°C, significant greenhouse gas (GHG) emission reductions will be needed across all sectors. This includes agriculture which accounts for a significant proportion of global GHG emissions. There is therefore a pressing need for the uptake of new technologies on farms to reduce GHG emissions and move towards current policy targets. Recently, precision livestock farming (PLF) technologies have been highlighted as a promising GHG mitigation strategy to indirectly reduce GHG emissions through increasing production efficiencies. Using Scotland as a case study, average data from the Scottish Cattle Tracing System (CTS) was used to create two baseline beef production scenarios (one grazing and one housed system) and emission estimates were calculated using the Agrecalc carbon footprinting tool. The effects of adopting various PLF technologies on whole farm and product emissions were then modelled. Scenarios included adoption of automatic weigh platforms, accelerometer-based sensors for oestrus detection (fertility sensors) and accelerometer-based sensors for early disease detection (health sensors). Model assumptions were based on validated technologies, direct experience from farms and expert opinion. Adoption of all three PLF technologies reduced total emissions (kg CO2e) and product emissions (kg CO2e/kg deadweight) in both the grazing and housed systems. In general, adoption of PLF technologies had a larger impact in the housed system than in the grazing system. For example, while health sensors reduced total emissions by 6.1% in the housed system, their impact was slightly lower in the grazing system at 4.4%. The largest reduction in total emissions was seen following the adoption of an automatic weight platform which reduced the age at slaughter by 3 months in the grazing system (6.8%) and sensors for health monitoring in the housed system (6.1%). Health sensors also resulted in the largest reduction in product emissions for both the housed (12.0%) and grazing systems (10.5%). These findings suggest PLF could be an effective GHG mitigation strategy for beef systems in Scotland. Although this study utilised data from beef farms in Scotland, comparable emission reductions are likely attainable in other European countries with similar farming systems.

Data-fusion for in-situ monitoring and molten state identification during LPBF of NiCoCr medium-entropy alloy

Laser powder bed fusion (LPBF) is an additive manufacturing technology with high practical value. In order to improve the quality of the fabricated parts, process monitoring has become a crucial solution, offering the potential to ensure manufacturing stability and repeatability. However, a cardinal challenge involves discerning a precise correlation between process characteristics and potential defects. This paper elucidates the integration of an off-axis vision monitoring mechanism via a high-speed camera focused on capturing the single-track melting phenomenon. An innovative image processing method was devised to segment the plume and spatters, while Kalman filter was employed for multi-object tracking of the spatters. The features of both the plume and spatters were extracted, and their relationship with molten states was investigated. Finally, the PSO-XGBoost algorithm was utilized to identify five molten states, achieving an accuracy of 92.16%. The novelty of this approach resides in its unique combination of plume characteristics, spatter features, and computationally efficient machine learning models, which collectively address the challenge of limited field of view prevalent in real production scenarios, thereby enhancing process monitoring efficacy. Relative to existing methodologies, the proposed PSO-XGBoost approach offers heightened accuracy, convenience, and appropriateness for the monitoring of the LPBF process. This work provides an effective and novel approach to monitor the LPBF process and evaluate the part fabrication quality for complex and changeable working conditions.

Life cycle assessment of magnesium phosphate cement production

Magnesium phosphate cement (MPC) is gaining attention in the field of rapid repair and coatings due to its superior qualities, yet little research has been done on the environmental impacts of MPC preparation. It's necessary to investigate its environmental implications for the sustainable development and application of MPC. This study develops a life cycle inventory of the production of MgO with three different production processes and the production of two phosphates and applies the inventory to conduct a life cycle assessment from cradle to gate for four scenarios of MPC production: Scenario 1 (S1) - dry route MgO and potassium dihydrogen phosphate (PDP) as raw materials; Scenario 2 (S2) - wet route MgO and PDP as raw materials; Scenario 3 (S3) - salt lake MgO and PDP as raw materials; Scenario 4 (S4) - dry route MgO and ammonium dihydrogen phosphate (ADP) as raw materials. Moreover, the sensitivity analysis and cost analysis of MPC are carried out. In order to comprehensively evaluate the low carbon properties of MPC, the carbon emissions of MPC and the commonly used rapid repair materials and coatings are compared. Results indicate S2 shows the highest carbon emission, and the global warming of S2 is 3.97%, 74.55%, and 1.41% higher than that of S1, S3, and S4, respectively. The environmental impact of MPC mainly comes from the production stage of MgO and phosphate (47%–67%). In three MgO production routes, S3 shows the best environmental performance. In two phosphates, S4 has a smaller impact on most impact categories. Sensitivity analysis also indicates that MgO and phosphate have a greater impact on the production of MPC. In addition, the cost of S2 is 2.91%, 0.37%, and 15.40% higher than that of S1, S3, S4, respectively, and S4 shows the best economic benefit. Furthermore, MPC has great advantages in rapid repair and coating, and can reduce carbon emissions by 12%–98% compared to other rapid repair materials and coatings.

Environmental Impact, Mechanical Properties, and Productivity: Considerations on Filler Wire and Scanning Strategy in Laser Welding

Abstract Sustainability, as well as high-quality outcomes, pose significant challenges within the context of current manufacturing cycles, in alignment with European strategies aimed at decarbonization. This framework encourages a systematic evaluation of manufacturing processes in terms of their performance and carbon footprint. One sector where this is particularly relevant is the production of batteries for electric mobility, thanks to its exponential growth. Out of all the processes involved, laser welding stands out as being a critical step since it offers potential energy savings through optimization. With the dual goals of achieving mechanical strength and environmental sustainability, this study investigates alternative solutions for laser welding of aluminum sheets. Different laser welding configurations are tested to evaluate the effect of process setups on weld quality and carbon emissions across different productivity scenarios. The key findings can be summarized as follows: (1) the selection of welding setup significantly influences both quality and sustainability requirements; (2) the optimal conditions for meeting strength requirements may diverge from those aimed at minimizing environmental impact; (3) the choice of the final solution is influenced by the specific industrial scenario. The study specifically demonstrated that aluminum alloys can be welded with higher quality (porosity below 1% and equivalent ultimate strength up to 204 MPa) when filler wire is introduced alongside an active wobbling scanning strategy. Conversely, filler wire can be omitted in scenarios prioritizing high-productivity and low-carbon emissions, such as when employing a linear scanning strategy, resulting in a reduction of equivalent carbon emissions by up to 140%.

Effect of ergonomic aspects on single‐ and multiproduct assembly‐line balancing problems

AbstractIncorporating ergonomic considerations into an assembly‐line balancing problem (ALBP) enhances productivity and minimizes ergonomic concerns. The assembly process, characterized by repetitive motions and handling numerous components, can lead to worker overload. Consequently, the inclusion of ergonomic aspects results in an appropriate distribution of assembly operations and relative workloads. This study investigates a multi‐objective ALBP aimed at minimizing the number of workstations, overall skill level required, and variance in workers' energy expenditure across workstations. To address the ALBP while considering the ergonomic aspects, this study proposes an approach based on the non‐dominated sorting genetic algorithm II (NSGA‐II) and multi‐objective simulated annealing (MOSA) using Pareto optimality. A comparative analysis of the NSGA‐II and MOSA is conducted in single‐ and multiproduct production scenarios, and a computational study involving various factors is performed to identify the dominant algorithm. The computational analysis indicates that the runtime performance of MOSA is 73.287% better than that of NSGA‐II; therefore, MOSA outperforms NSGA‐II. This study aims at applying scientific knowledge concerning manufacturing ergonomics to assist manufacturing industries in enhancing their productivity.

Evaluating cotton apparel with dynamic life cycle assessment: The climate benefits of temporary biogenic carbon storage

Static life cycle assessment (LCA) methodologies fail to consider the temporal profiles of system inputs and outputs (including emission timing), such that they underestimate the benefits of temporarily stored biogenic carbon in bioproducts, such as cotton. This research focuses on greenhouse gas emission timing and applies dynamic emission accounting to the life cycle of cotton woven pants. The significance of temporary biogenic carbon storage and emission timing is illustrated by converting the 2017 Cotton Incorporated static LCA to a dynamic model using the Dynamic Carbon Footprinter (baseline scenario). A reduction in cumulative radiative forcing for dynamic relative to static modeling of 22%, 5%, and 2% are observed at 10-years, 30-years, and 100-years, respectively. Alternative scenarios analyzed include converting cotton woven pants at end of life to bioenergy, to compost, or to building insulation, an alternative cotton production scenario using regenerative agricultural practices, and two pants extended lifetime scenarios. The regenerative agricultural practice scenario provides reductions in cumulative impacts compared to the baseline scenario of 96%, 69%, and 105% after 10, 30, and 100-years, respectively. A 3x extension in the lifetime of pants provides a benefit in reduced cumulative impacts of 31%, 40%, and 41%, after 10, 30, and 100-years, respectively. This case study with cotton demonstrates that dynamic LCA is a useful tool for assessing the benefits of biobased products, and it allows for more nuanced analysis of reductions in climate impacts in both the short- and long-term time horizons.

Forest management for water yield: Assessing the barriers and impacts of privately-owned open pine woodlands in the Southeastern United States

In the Southeastern United States, freshwater resources are stressed due to human development, population growth, expansion of agricultural irrigation, and climate change. Restoring land to a low-density, fire-maintained longleaf pine (Pinus palustris) savanna and woodland ecosystem may provide distinct ecosystem services such as increases in water quantity for vulnerable watersheds. To understand how economic incentives may bolster restoration programs, we analyze the economic and hydrologic impacts of longleaf pine restoration scenarios. We compare these scenarios against other common land uses in the Coastal Plain of Georgia, a major forestry state located in the Southern United States, identifying the financial barriers and water use impacts of alternative forest management regimes. Longleaf pine restoration shows the greatest increase in water yield per acre at 66 % over baseline timber production scenarios but at a lower timber value return. Thinning planted loblolly pine (Pinus taeda) stands to low-density woodlands may offer a more cost-efficient, immediate alternative that improves water yields by 38 %. Current incentives to restore native habitats on private lands increase financial returns but lack sufficient incentives and governance to meet or exceed baseline forest income levels by $90-$235 ha-1 annually for longleaf savanna restoration. In the future, the emerging ecosystem service market in Georgia may steer decisions about competing values of carbon storage, water yield, and native habitat restoration.