Improve Resource Efficiency Research Articles

Gear and bearing power losses: from dip to minimum quantity lubrication

PurposeThis paper aims to address the influence of lubrication methods on operational characteristics, power losses and temperature behavior of gears and bearings. It contributes to the improvement of resource and energy efficiency of geared transmissions.Design/methodology/approachExperimental investigations were performed at a gear and bearing power loss test rig. Thereby, dip lubrication, injection lubrication with injection volumes from 0.05 to 2.00 l/min and minimum quantity (MQ) lubrication with an injection volume as little as 28 ml/h were considered. Measurements were evaluated in terms of no-load and load-dependent power loss, bulk temperatures and mean gear coefficients of friction.FindingsResults show strongly reduced no-load gear and bearing losses for lubrication methods with low lubricant quantities. Load-dependent losses are similar to conventional lubrication methods and tend to be lower at high speed. This is related to higher bulk temperatures, as the heat dissipation of lubrication methods with low oil quantities is limited. Limited thermal load limits were shown to be extended by LowLoss gears.Originality/valueSystematic investigations were conducted to evaluate the influence of dip, injection and MQ lubrication on power loss and temperature behavior of gears and bearings. The results of this study support further research on needs-based lubrication methods for gearboxes.

Evolution of the Anthropogenic Gallium Cycle in China From 2005 to 2020

Gallium is deemed to be a critical mineral given its irreplaceable use in consumer electronics and clean energy technologies. China has become a significant consumer of gallium while also playing a leading role in global gallium production, accounting for more than 90% of the world’s primary output in recent years. However, the quantification and evolution of China’s gallium cycle is limited until now. This study aims to uncover the dynamic flows and stocks of gallium in China during the period of 2005–2020. The results reveal that: 1) From 2005 to 2020, China’s gallium demand increased more than 20-fold, as a result of the booming semiconductor industry and the surging use of gallium in some low-carbon technologies; 2) despite the inefficient recovery issues existed in the production stage, the supply of gallium extracted as a byproduct grew in tandem with the capacity of alumina production, resulting in a significant supply surplus of 948 t by 2020; 3) China exported nearly half of its gallium mainly as raw materials and final products, but still experienced a high reliance on imported gallium-containing intermediate products from abroad, such as integrated circuits; 4) the generations of in-use stocks and end-of-life flows of gallium have accelerated since 2005 and reached about 278 t and 169 t in 2020, respectively. These indicate a large amount of available secondary gallium resource, with nonexistent recycling. The results provide a basis for identifying gallium extraction, use, loss and recycling within its anthropogenic cycle in China, as well as guidance for stakeholders to make future decisions concerning ways to improve resource efficiency and promote sustainable gallium practices from a dynamic material cycle perspective.

Modelling UK electricity regional costs for commercial buildings

As electricity prices continue to rise, it is essential that UK non-domestic consumers develop smart energy management practices. However, most of these consumers lack awareness of the spatial–temporal dynamics of electricity prices and their tariff components. To help overcome these barriers and contribute to energy price digitalisation, this paper presents a Modelling UK Electricity Regional Costs (MUKERC) framework. A bottom-up methodology that defines all the tariff components and then aggregates them to quantify the cost of a kWh across each half-hour of the day. The framework not only facilitates understanding which tariff components have a higher impact during different time periods, but also depicts how they vary spatially across regions. This model was used to estimate and analyse the evolution of electricity costs from 2017 to 2024. Case studies from buildings in the education sector are showcased depicting their energy costs derived from their load profiles. Results show that the London area has the lowest average prices, while the Northern Wales & Merseyside is the most expensive. From the case studies conducted, peak period charges account for 17% of annual electricity costs (occurring between 4 and 7 p.m.). Winter period charges represented about 53% of the charges. The MUKERC framework showcases the valuable insights data-driven costing models offer as it allows to understand the dynamics of electricity charges and identifies “when” and “where” the cost of electricity is more expensive; thus, supporting the development of bespoke cost-effective energy measures that improve resource efficiency and smart energy management initiatives.

Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control Approach

Due to the proliferation of requests in heterogeneous resources in edge computing, the existence of a large number of tasks and workloads in virtual machines in the edge computing environment is inevitable. Thus, load balancing strives to facilitate an even distribution of workload across available resources. Its purpose is to provide continuous service and to ensure fair load distribution among resources. Load balancing, with the aim of minimizing response time for tasks and improving resource efficiency, tries to do the proper mapping of tasks among virtual machines at a lower cost. Flow scheduling, on the other hand, assigns a task (group of tasks) to computational resources by prioritizing tasks, so that the relationship between them is maintained. Therefore, in this research, a hierarchical control framework for load balancing and assignment of tasks in edge computing services is presented in order to create load balancing. In the proposed method, in the first level, the genetic algorithm receives a set of tasks in a workflow. Genetic algorithm prioritizes and assigns tasks to resources according to time constraints, resource processing power, resource availability, and task cost. For this purpose, the integer linear programming optimization in the evaluation function of the genetic algorithm will be utilized. In the second level, considering the past load distribution in edge resources, we estimate the probability of load distribution among sources according to hidden Markov model (HMM). Finally, in order to optimally map tasks to the virtual machines in each host, we will use game theory with service quality factors as an evaluation function. Previous methods have provided a hierarchical control framework that aims to achieve conflicting goals within a data center, but does not use linear programming. Considering the use of service quality criteria as evaluation function parameters in heuristic and optimization methods in this research, it is expected that the results of this research will improve compared to previous methods.

Relationship between the digital economy, resource allocation and corporate carbon emission intensity: new evidence from listed Chinese companies

Many studies have focused on the relationship between the digital economy and carbon emissions at the macro level. However, there is a relative dearth of research on this relationship at the micro level. In this study, we determined the impact of the digital economy on the carbon emissions of individual companies and the mediating role of resource allocation in this relationship using data from listed Chinese manufacturing companies between 2011 and 2019. This analysis yielded three main findings. First, based on firm-level carbon emissions and the borderless organization theory, we found that the digital economy significantly reduced corporate carbon emission intensity. Second, the digital economy reduced resource misallocation and improved resource efficiency, which in turn reduced corporate carbon emission intensity. Third, market drivers and government regulations improved and hindered the ability of the digital economy to reduce corporate carbon emission intensity, respectively. These findings provide evidence for the need for government investment in the development of digital technologies and corporate digitization; the use of digital technologies by businesses to improve resource and energy efficiency; and minimal government regulation of the digital economy in favor of self-regulation through market forces. These measures are important for economic transformation and the achievement of carbon neutrality in emerging developing countries, including China.

Socioeconomic and resource efficiency impacts of digital public services.

This paper measures the socioeconomic and resource-efficient influences of digital transformation in the public sector in the European region. To capture the socioeconomic impacts of digital public services, we employ a socioeconomic score index calculated as the unweighted average of the re-scaled scores for changes in employment, exports, and turnover from eco-industries. Regarding resource-efficient impacts, we employ the resource efficiency score index measured as the unweighted average of the re-scaled scores for material, energy productivity, and the intensity of greenhouse gas (GHG) emissions. Measures such as user-centricity, business mobility, and key enablers are used to demonstrate the level of digitalization in the public sector. According to our estimations based on various econometric techniques, digital public services have a favorable effect on the economy and society through a positive impact on employment, exports, and turnover of eco-industries. The effects of digitalization on resource productivity follow a nonlinear U-shaped curve, suggesting that the improvement of resource efficiency is only present when the digital transformation process reaches a certain level.

Construction Waste Management for Improving Resource Efficiency in The Reconstruction of War-Destroyed Objects

Introduction. Social and hostility conflicts in Ukraine have caused global changes and deterioration of the ecological and economic situation in numerous countries, resulting in higher risks to the population and giving the impetus to create a new innovative, cultural, and technological society. Greening principles, which have become popular in developed countries, are impossible to implement without the coordinated cooperation of states and their genesis in accordance with joint programs and development vectors. As a result, an increasing number of foundations and grants have recently been established to support eco-friendly products and solutions, assist in their adoption, and develop the organizational structure of an efficient system based on green economy principles, thereby improving global standards and regulations.
 Aim and tasks. The aim of the study is to develop a methodology for planning the modernization of constructed infrastructure, better resource efficiency, and reduce waste in the reconstruction of war-destroyed objects.
 Results. The best option for post-war waste disposal is the construction of mobile waste recycling complexes based on fractional waste recycling and its secondary use in further reconstruction of Ukrainian infrastructure, which after final restoration will be moved to safe areas around large cities and used as stationary waste recycling plants. To improve the environmental situation, one suggests building landscaped parks that will operate due to state-of-the-art technologies (obtaining natural energy, etc.) and creating a system of harmonious combination of anthropogenic infrastructure and natural areas, businesses, and public accessibility.
 Conclusions. The key goal of the reconstruction of Ukrainian territories is to establish an innovative, sustainable, and safe system of social existence by increasing the efficiency of actions and methods based on the principles of rational environmental management and increasing responsibility. When calculating the cost of the restoration of Ukrainian territories, one should take into account additional expenses caused by hostilities, which will be further considered as a stress factor having a cumulative negative impact and leading to disastrous consequences that can be estimated in monetary equivalent as the sum of the price of restoration of components of the ecological, economic, and social system under the effect of a set of factors resulting in negative changes and damages.

Does foreign direct investment matter for environmental innovation in African economies?

This study investigates the role of foreign direct investment (FDI) in environmental innovation in Africa during 1990–2019. It utilizes the endogenous growth theory to specify an innovation production function, estimated using the seemingly unrelated regression (SURE) method. The study employs four indicators of environmental innovation and also controls for the influence of resource abundance. Key findings from the study show evidence that FDI inflow enhances environmental innovation practices by improving resource efficiency outcomes. In particular, FDI is found to reduce greenhouse gas emission intensity of output and carbon intensity of energy. Further, the effect of FDI on resource utilization and energy productivity is insignificant. Estimates confirm the learning and imitation, and demonstration effects of FDI on resource utilization, though the formal effect is detrimental. The labour market effect is revealed to promote resource efficiency, while resource abundance plays negligible role in environmental innovation in all models. Policy implications are derived.

Machine learning model for malaria risk prediction based on mutation location of large-scale genetic variation data

In recent malaria research, the complexity of the disease has been explored using machine learning models via blood smear images, environmental, and even RNA-Seq data. However, a machine learning model based on genetic variation data is still required to fully explore individual malaria risk. Furthermore, many Genome-Wide Associations Studies (GWAS) have associated specific genetic markers, i.e., single nucleotide polymorphisms (SNPs), with malaria. Thus, the present study improves the current state-of-the-art genetic risk score by incorporating SNPs mutation location on large-scale genetic variation data obtained from GWAS. Nevertheless, it becomes computationally expensive for hyperparameter optimization on large-scale datasets. Therefore, this study proposes a machine learning model that incorporates mutation location as well as a Genetic Algorithm (GA) to optimize hyperparameters. Besides that, a deep learning model is also proposed to predict individual malaria risk as an alternative approach. The analysis is performed on the Malaria Genomic Epidemiology Network (MalariaGEN) dataset comprising 20,817 individuals from 11 populations. The findings of this study demonstrated that the proposed GA could overcome the curse of dimensionality and improve resource efficiency compared to commonly used methods. In addition, incorporating the mutation location significantly improved the machine learning models in predicting the individual malaria risk; a Mean Absolute Error (MAE) score of 8.00E−06. Moreover, the deep learning model obtained almost similar MAE scores to the machine learning models, indicating an alternative approach. Thus, this study provides relevant knowledge of genetic and technical deliberations that can improve the state-of-the-art methods for predicting individual malaria risk.

Improvement in feed efficiency and reduction in nutrient loading from rainbow trout farms: the role of selective breeding.

Resource efficiency, the ratio of inputs to outputs, is essential for both the economic and environmental performance of any sector of food production. This study quantified the advancement in the feed conversion ratio (FCR) and reduction in nutrient loading from rainbow trout farming in Finland and the degree to which genetic improvements made by a national breeding program have contributed to this advancement. The study combined two datasets. One included annual records on farm-level performance of commercial rainbow trout farms from 1980 onwards, and the other included individuals across eight generations of the national breeding program. The data from the commercial farms showed that from 1980 onwards, the farm-level feed conversion ratio improved by 53.4%, and the specific nitrogen and phosphorus loading from the farms decreased by over 70%. Hence, to produce 1 kg of fish today, only half of the feed is needed compared to the 1980s. The first generation of the breeding program was established in 1992. The FCR was not directly selected for, and hence, the genetic improvement in the FCR is a correlated genetic change in response to the selection for growth and body composition. Since 1992, the estimated genetic improvement in the FCR has been 1.74% per generation, resulting in a cumulative genetic improvement of 11.6% in eight generations. Genetic improvement in the FCR is estimated to be 32.6% of the total improvement in the FCR observed at farms, implying that genetic improvement is a significant contributor to resource efficiency. The use of genetically improved rainbow trout, instead of the base population of fish, reduces feed costs by 18.3% and total production costs by 7.8% at commercial farms (by −0.266€ per kg of ungutted fish). For phosphorus and nitrogen, it can be assumed that the use of fish material with an improved FCR also leads to 18.3% less nitrogen and phosphorus flowing into an aquatic environment. Such improvements in resource efficiency are win–wins for both industry and the environment—the same amount of seafood can be produced with significantly reduced amounts of raw materials and reduced environmental impact.

Resource efficient collagen extraction from common starfish with the aid of high shear mechanical homogenization and ultrasound

Processes currently used for collagen extraction are complicated requiring a great deal of time and chemicals. Here, high shear mechanical homogenization (HSMH) and ultrasound (US) were integrated in the pretreatment step of collagen extraction from common starfish to reduce chemical use and time consumption. Effects of the assistant technologies on yield, structural integrity and functionality of collagen were also investigated. HSMH reduced the deproteinization time from 6 h to 5 min and its required amount of alkali 4 times, compared with classic methods. HSMH + US reduced the demineralization time from 24 h to 12 h and improved its efficiency in extraction of minerals. Collagen extraction with HSMH and HSMH + US resulted in similar yield as the classic method and did not affect triple helical structural integrity, polypeptide pattern, thermal stability or fibril-formation capacity of the collagens. Altogether, HSMH and US can effectively improve resource efficiency during collagen extraction without imposing negative effect on collagen quality.

An investigation of the relationship among economic growth, agricultural expansion and chemical pollution in Iran through decoupling index analysis.

Due to the significant role of agricultural chemicals in increasing agricultural production and ensuring food security, the excessive use of chemical fertilizers and pesticides has been intensified in Iran. These chemical inputs are important environmental pollutants that threaten human health. In the recent years, in agricultural sector, the balance between the growth of agricultural economy and the spread of pollution in Iran has been one of the major challenges. In this regard, the use of decoupling index to decouple the link between agricultural production and pollution caused by the consumption of chemical inputs, such as fertilizers and pesticides, has been emphasized; Therefore, in the present study, the decoupling index first is calculated in relation to the emission of pollution caused by the use of chemical inputs in the process of agricultural production during the period of 1991-2016 in Iran. Then, by reviewing the existing literature systematically, the factors affecting the decoupling index in the agricultural sector of Iran are evaluated using the autoregressive distributed lag (ARDL) model. The results showed that in the recent years, pollution indicators in relation to chemical inputs have not had ideal trends, and despite the further growth of agricultural production, the quality of the environment has experienced a declining trend. The results of the decoupling index related to the use of chemical pesticides and fertilizers in Iran show that during a period of 26-year, only 5 and 4years of using these inputs have had a sustainable state compared to the production growth; besides, a strong negative decoupling state occurred as the most unsustainable state in relation to chemical fertilizer for 7years. Moreover, among the factors affecting the decoupling index, the value-added variable of the agricultural sector has had the most positive effect on this index, and thus, in the long run, it increases the level of pollution in the agricultural sector. The variables of gross domestic product (GDP) per capita and the area under cereal cultivation in the agricultural sector would also increase the decoupling index. Accordingly, adopting effective strategies to improve resource efficiency, planning for the implementation of biotechnological methods, and doing investment for creating green infrastructure in the agricultural sector can be effective in the ideal decoupling of pollution and agricultural economy growth in Iran.

Understanding the effects of site-scale water-sensitive urban design (WSUD) in the urban water cycle: a review

AbstractWith city growth, the development of vacant or under-used land parcels is becoming more common compared to the past. The current ‘water-sensitive urban design (WSUD)’ approach to such development will improve resource efficiency, liveability, and the amenity of cities, especially natural water systems. However, there is a need to quantify the water performance of site-scale WSUD options, especially about how these options impact the ‘natural’ and ‘anthropogenic’ flows in the urban water cycle. This study reviewed research about site-scale applications, summarizing the urban water cycle studies from before development to after development. Key findings (i) include very big margin was quantified by (a) water retention (30–100%) and (b) portable water demand reduction (18–100%) for selected site-scale WSUD options through six research studies; (ii) still unclear about the selected site-scale WSUD options’ interaction performance in the urban water cycle between each water accounts, and (iii) need to clarify the site-scale WSUD option's contribution under specific rainfall scenarios. In summary, this study aims to review the literature on the urban water cycle; review the effects of site-scale WSUD options in the urban water cycle; review the water mass balance and relevant evaluation application, and highlight the opportunities for the future urban water cycle studies.

Impact of Industry 4.0 on corporate environmental sustainability: Comparing practitioners’ perceptions from China, Brazil and Germany

Industrial production needs to be fundamentally transformed if the UN Sustainable Development Goals shall be met. Digital technologies can potentially drive such a sustainable transformation, but two main objectives of environmental sustainability must be considered for achieving this target: decarbonisation and dematerialisation. We empirically investigate the potentials for these two objectives by employing a survey-based approach, investigating companies’ developments in China, Brazil, and Germany, in a variety of industrial sectors, and in companies of different sizes. These cases provide insight on a multi-country perspective into developments in digitalisation in countries with different pre-conditions for digital transformation, which is a novelty to this research field. We show that even though most industrial practitioners expect an improvement of the environmental sustainability of their respective company due to the application of Industry 4.0 technologies, factual improvements in resource efficiency and energy consumption are not expected to develop in a similarly optimistic fashion. These findings challenge the assumed effects of Industry 4.0 discussed in the vast majority of prior literature which expresses high hopes for positive impacts on resource efficiency and energy consumption . This can be interpreted as an indication that Industry 4.0 will not automatically lead to environmental improvements instead this transformation towards a more sustainable economy needs to be accompanied by supporting measures. On the positive side we find that the higher the current Industry 4.0 level of companies, the greater their ability to match their supply with the actual demand and their likelihood for participating in Demand Response schemes. This is an important prerequisite for the stabilisation and efficient use of future renewable energy systems. Our study provides insights to policy makers and practitioners but also fellow researchers regarding current trends in the implementation of Industry 4.0 and in how far they support the transformation towards more sustainability. We conclude that the implementation of the Industry 4.0 concept should always be critically evaluated against the background of the SDGs and must be supplemented by a combination of regulation and incentives through governing bodies, which includes setting binding targets for saving energy and material and reducing non-recyclable waste.

Energy self-sufficiency and greenhouse gas emission reductions in Latin American dairy farms through massive implementation of biogas-based solutions

The transition towards sustainable economies with improved resource efficiency is today’s challenge for all productive sectors. The dairy sector in Latin America is growing without considering a clear path for sustainable energy and waste management solutions. This study proposes integrated solutions through a waste-to-energy approach. The solutions consider biogas production (via cow manure) as the main energy conversion pathway; technology solutions include biodigesters, power generators, and combined heat and power systems that supply not only the energy services demanded by dairy farms (for cooking gas, electricity, refrigeration and hot water) but also provide organic fertilizers. Biogas’ potential was estimated to verify whether it can cover the energy demands of the farms, while the levelized costs of producing biogas and electricity were the indicators for the techno-economic evaluation of the solutions. Greenhouse gas emission reductions were estimated by following IPCC guidelines. Specifically, the proposed solutions lead to energy self-sufficiency in most dairy farms with relevant biogas and electricity costs in the range of 1.7–3.7 and 6–12 USD cents/kWh, respectively. In addition, implementing the proposed solutions in Latin American dairy farms would allow annual greenhouse gas emission reductions of 32.8 Mton CO2 eq. with an additional 17 Mton if widespread use of the supplied organic fertilizers is achieved.

Rateworkspace: BIM integrated post-occupancy evaluation system for office buildings

The feedback obtained from occupants regarding their comfort needs and performance of buildings is critical for assessing occupant satisfaction, identifying the operation and maintenance (O&M) issues in time and for improving resource efficiency in buildings. Current facility management (FM) systems and occupant feedback collection practices, however, have limitations in supporting effective decision-making in FM, as they lack the necessary contextual data related to the occupant feedback (e.g., building geometry, systems, elements). Building Information Modeling (BIM)-enabled FM systems are used for combining different types of FM information with building models; however, occupant feedback is still not effectively utilized in FM since it is not integrated with BIM. In this study, a BIM integrated post-occupancy evaluation system prototype is developed for: (1) collecting occupant feedback along with the contextual information related to the feedback items in a structured way, and (2) presenting this information as integrated with BIM to the facility managers. This enables conducting spatio-temporal queries and supports effective decision-making by visualizing the collected feedback. The prototype was designed by using qualitative shadowing with FM teams to identify information needs and use case analysis to determine how contextual data integrated with BIM could be collected from office occupants who are non-technical persons with limited information on building models. This paper identifies the FM query categories that are required to process the occupant feedback and describes the RateWorkSpace prototype developed for office buildings. The deployment of the prototype in a real-world office demonstrates that the proposed system is applicable, practical, usable, and that real-time building performance data can be both collected and analysed with the developed system. This has the potential to increase the effectiveness of the FM and O&M processes, and help to create office spaces with optimized energy use and occupant comfort that also supports occupant well-being and productivity.

Potential of Using Selected Industrial Waste Streams in Loop-Closing of Material Flows—The Example of the Silesian Voivodeship in Poland

Every year, the industrial sector produces a significant amount of waste. Bearing in mind the need to improve resource efficiency and reduce the impact of industrial plants on the environment, an attempt was made to identify and determine the processing potential of selected waste streams with respect to the circular economy, along with an indication of sectoral connections. The subject of the study was industrial waste, which is the dominant stream of waste generated in the Silesian Voivodeship—the area of research. The paper presents the results of quantitative and qualitative analysis of the selected industrial waste streams and provides a visual representation of the flow. The study was based on available statistical data covering quantities of industrial waste. The article also includes the characteristics and possibilities of closing their circulation in the economy. The conducted SWOT analysis identified factors that strongly affect the possibility of implementing the circular economy model. The research allowed us to indicate possible waste flows and identify sectors closing their loop according to the assumptions of industrial symbiosis. The research path carried out on the example of Silesia shows the potential of the model and thus the possibility of implementation in other locations. This study showed that the strongest connection exists between mining-related plants, industrial power plants and combined heat and power plants, cement plants, and construction plants, especially road construction.

Advancing urban metabolism studies through GIS data: Resource flows, open space networks, and vulnerable communities in Mexico City

AbstractUrban metabolism studies provide valuable insights that can improve resource efficiency at the city scale. However, only a limited number of such studies include spatially explicit data to inform planning practitioners. In this article, we argue that integrating spatially explicit urban metabolism data in urban planning can leverage resource‐efficient development and management of open space networks. Based on this premise, our research presents a methodological strategy to investigate how the use of GIS data can improve the applicability of metabolic studies in urban planning and the management of open space networks in particular. A GIS‐based urban metabolism assessment of Mexico City was performed at the city scale, including data on vulnerable communities, communal lands, and indigenous areas. After mapping selected GIS layers, a detailed resource‐efficiency analysis was performed through the compilation of a Borough Pattern Scan, based on quantification of resource use, total areas of resource infrastructure, and public open spaces. The results of our multiscale spatially explicit analysis provide an improved understanding of borough metabolic profiles, which can leverage a more resource‐efficient development of open space networks in Mexico City.

How Do Firms Achieve Green Innovation? Investigating the Influential Factors among the Energy Sector

This research aims to examine the direct impact of green innovation strategies and the green dynamic capability on green innovation. The indirect effect is also tested, using green organizational identity as a mediator. A cross-sectional and quantitative research approach was used to collect data from energy firms. The results show that green innovation strategies and green dynamic capabilities positively affect the green innovation. The findings also proved that a green organizational identity acts as a mediator. The research outcomes suggest that the role of green organizational identity needs to be verified by firm managers who want to boost green innovation. In the advance era, society is more conscious about the green environment. Managers need a green innovation strategy to achieve green innovation within their firm. This study will help to manage the environmental issues, environmental degradation reductions, and establishment of green programs and products, which will all benefit society in the terms of improving resource efficiency, enhancing quality of life, and encouraging economic development.

Applying probabilistic material flow analysis for quality control and management of waste recycling in steelmaking

In modern steelmaking, multiple processes comprise a continuous manufacturing system, but not all phosphorus content data are connected or integrated into a holistic and systematic database. Disconnected data hinder the improvement of material management and resource efficiency in the industry. The objective of this study was to establish a method to evaluate material flows, reduce uncertainty, and perform quality control for waste recycling in the steelmaking industry. The results indicate that 10% of the phosphorus input is present in the final products, 30% accumulates in the slags, and more than 60% of the total mass remains in the processes. Comparing the material flow analysis results obtained using static and probabilistic approaches, the partition ratio of the phosphorus content in slags changes from 24.07% to 40.78%, but that in processes changes from 49.10% to 68.05%. This indicates that the variations in phosphorus content in slags and processes might affect the effectiveness of slag recycling and might increase the resource consumption required to maintain the quality of final products. The probability of forming substandard products in the baseline scenario is 0.43. Adopting a 50% removal rate, the probabilities of forming substandard products are reduced to 0.36 (waste removal scenario), 0.38 (slag reduction scenario), and 0.31 (raw material treatment scenario). The performance of raw material treatment and waste removal is more efficient for quality control. The method used in this study can be applied to evaluate the possible outcomes of waste recycling and reduce the probability of forming substandard products.