Assessing the Global Sustainability Impact of Improving the Secondary Steel Production: Lessons Learned from an Italian Steel Plant

This study presents a comprehensive sustainability assessment of a series of technical interventions aimed at improving a secondary steel production process using the Electric Arc Furnace (EAF) technology in a steel plant located in northern Italy. The assessment covers the environmental, social, and economic dimensions of sustainability by considering three sets of indicators and employing a multi-criteria decision-making approach. The results show that the considered interventions can lead to significant improvements in the sustainability performance of the EAF process. The study also highlights the trade-offs and synergies among the sustainability dimensions and provides recommendations for decision-makers to promote sustainable practices in the steel industry. Overall, this study underscores the importance of addressing the sustainability challenges faced by energy-intensive industries such as steel production

Organic fertilizer effect on lettuce (Lactuca sativa L.) cultivated in nutrient film technology

The survey was carried out in the Research Centre for the study of food products quality “HORTINVEST” between 2014-2015 using the Nutrient Film Technology (NFT) for the cultivation of lettuce. Three lettuce cultivars were used: Markies, Lollo bionda and Lollo rosa, together with three types of fertilizers: two organic fertilizers and a chemical one. Biometrical measurements on lettuce growth were conducted; also at the end of the cultivation cycle biochemical measurements were performed in order to assess plant quality. Also correlations between various biometrical parameters and influencing vegetal factors were settled. The results led towards gains in the plant growth rate, in the development of the leaf number and eventually in the production growth. For all lettuce varieties under research the total plant mass was higher due to organic fertilizers in comparison with plants under chemical fertilizer treatment. The plants organically fertilized proved to be superior as to the biochemical quality. Research was carried out in order to assess the quantitative and qualitative feedback of lettuce cultivated in Nutrient Film Technology (NFT) to various organic fertilizers which might replace chemical fertilizers

Application of piezosurgery in the extraction of impacted canines (case report)

Introduction: The permanent canines and wisdom teeth are most affected by impaction. Regarding the impacted canines, orthodontic traction and self-transplants are potential treatment options but many factors have to be considered when performing these interventions: patient age, root formation stage and tooth development, angulation, absence of root dilacerations, extensively of the osteotomy, etc. In cases where these two treatment options are unpredictable or contraindicated, a surgical extraction of the impacted tooth is a method of choice. Aim: The aim is to present a piezosurgery-assisted extraction of impacted canine evaluating advantages and disadvantages when compared to osteotomy with conventional rotatory instruments. Material and methods: A 14 years-old female was referred to our clinic with complaint for absence of the right maxillary canine. CBCT scan revealed impacted maxillary right canine, Class III by Archer. Due to the unfavorable position and curved root, orthodontic traction treatment was considered as contraindicated, so we proceeded with a tooth removal using piezo surgical device. Results: Piezosurgery showed less damaging of adjacent tissue and less heating during the procedure, shortened postoperative period and patient discomfort, as well as lower inflammatory response. Conclusion: Taking into account the advantages over disadvantages of using the piezo approach for extraction of impacted canines, we can recommend this method with full confidence and predictable outcomes

A Review of Energy and Environmental Management Practices in Cast Iron Foundries to Increase Sustainability

Environmental impact and use of energy and materials are relevant topics in companies. To achieve energy savings and enhance environmental performance, managers can invest in technologies (technical measures) and/or implement management practices (low-cost and non-technical measures). This paper focuses on energy and environmental management practices in foundry, which is a particularly energy-intensive industry producing significant carbon dioxide emissions. We conducted a scoping review of scientific publications and technical documents to identify practices that enable energy efficiency improvement and adverse environmental impact reduction in cast iron foundries using coreless induction furnaces. The review returned 399 practices, which we categorised according to the process step of application and theme. We developed a hierarchy to classify the practices according to their sustainability. The results show that the practices proposed in the literature focus mainly on avoiding or reducing resource consumption, rather than on recovering residual value. The intended contribution is to promote the adoption of management practices as an effective lever to increase energy efficiency and reduce environmental impacts, while also providing a summary of current knowledge to facilitate the identification of areas for further research. The review could also support foundry managers in the selection and prioritisation of the practices to adopt

ProMetaUS: A proactive meta-learning uncertainty-based framework to select models for Dynamic Risk Management

Safety managers, practitioners, and researchers can employ different models for estimating and assessing hazards, consequences, likelihoods, risks, and/or mitigation measures in the safety field. The selection of a specific model may depend on the uncertainty associated with its estimation and its impact on the safety-related decision-making process. The recognition of this issue as an example of Algorithm Selection Problem (ASP) allows investigating the applicability of meta-learning principles that are scarcely adopted in the risk and safety literature. Consequently, we propose a novel meta-learning inspired framework to proactively rank a set of candidate models for Dynamic Risk Management (DRM) based on desired uncertainty conditions. We denominate this framework ProMetaUS (Proactive Meta-learning and Uncertainty-based Selection for dynamic risk management). To achieve this purpose, our meta-learning system acquires knowledge that relates the characteristics extracted both directly and indirectly from datasets (e.g. data-based, domain-based, simple and fast uncertainty-based, simple and fast sensitivity-based meta-features) to some performance measures of the models. Performance measures include confidence information, shape measurable quantities, safety decision criteria and threshold limits, and sensitivity analysis outputs. We tested the proposed framework in a case study about Oxygen Deficiency Hazard (ODH) assessment by means of @RISK. For each of the five datasets, single-performance measure rankings and a final ranking of the three models are generated. Such rankings are aggregated to obtain the global recommended ranking

Integration Between Occupational and Process Safety: Existing Approaches and Challenges for an Enhanced Framework

Occupational safety is traditionally managed distinctly from process safety. The former puts the emphasis on high frequency and low consequence incidents, and refers to hazards that could provoke human consequences. In contrast, the latter focuses on releases of chemicals, energy, or hazardous materials, leading to low likelihood but high consequence accidents with severe negative effects to people, environment, assets, and/or business. However, neither personal safety nor process safety should be compromised, and their common priority should remain the elimination and mitigation of adverse consequences for workers. The aim of this paper is to provide an overview of existing approaches for harmonising the assessments of personal and process safety risks, pointing out the main challenges to be addressed for developing a future enhanced framework. To achieve this aim, a systematic literature review in databases of scientific publications was carried out. The review returned 14 studies, most of which propose a new method or tool (e.g., risk index). The critical analysis of these results indicated the strengths and weaknesses of each approach that represent helpful starting points for an enhanced framework. Such a framework should systematically identify hazards, hazardous events, and exposure conditions that cause harm to employees, and should establish a ranking of the risks according to their criticality for prioritising risk management efforts

ProMetaUS: A proactive meta-learning uncertainty-based framework to select models for Dynamic Risk Management

Safety managers, practitioners, and researchers can employ different models for estimating and assessing hazards, consequences, likelihoods, risks, and/or mitigation measures in the safety field. The selection of a specific model may depend on the uncertainty associated with its estimation and its impact on the safety-related decision-making process. The recognition of this issue as an example of Algorithm Selection Problem (ASP) allows investigating the applicability of meta-learning principles that are scarcely adopted in the risk and safety literature. Consequently, we propose a novel meta-learning inspired framework to proactively rank a set of candidate models for Dynamic Risk Management (DRM) based on desired uncertainty conditions. We denominate this framework ProMetaUS (Proactive Meta-learning and Uncertainty-based Selection for dynamic risk management). To achieve this purpose, our meta-learning system acquires knowledge that relates the characteristics extracted both directly and indirectly from datasets (e.g. data-based, domain-based, simple and fast uncertainty-based, simple and fast sensitivity-based meta-features) to some performance measures of the models. Performance measures include confidence information, shape measurable quantities, safety decision criteria and threshold limits, and sensitivity analysis outputs. We tested the proposed framework in a case study about Oxygen Deficiency Hazard (ODH) assessment by means of @RISK. For each of the five datasets, single-performance measure rankings and a final ranking of the three models are generated. Such rankings are aggregated to obtain the global recommended ranking

Qualitative risk assessment of a Dual Fuel (LNG-Diesel) system for heavy-duty trucks

A Dual Fuel (LNG-Diesel) system can be applied to heavy-duty diesel trucks for achieving environmental benefits in comparison to existing diesel vehicles. Because of lack of reports about risk assessment of this technology, we performed a qualitative assessment based on a framework of some literature techniques for risk identification, analysis and evaluation. After constructing a Reliability Block Diagram (RBD) to establish the context, we conducted bow-tie analysis, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), likelihood and consequence analysis, and used a risk matrix. We applied these methods and techniques qualitatively to identify causes (e.g. collisions), critical events (e.g. releases of natural gas), related consequences (e.g. fires and explosions), and different possible pathways from a specific cause to its consequence, and to assess some negative accident scenarios related to use and parking of the vehicle. The bow-tie analysis also allowed to make explicit barriers and controls that prevent critical events and/or mitigate consequences. Therefore, we identified a set of safety measures, including design, technical, management, and emergency actions, which shall be implemented in each step of the system's life cycle.Our risk assessment showed that the risk level of the Dual Fuel (LNG-Diesel) system is similar to the risk level of a traditional diesel system. Future research will overcome current lack of data and, therefore, permit a quantitative rating of the risk of the Dual Fuel (LNG-Diesel) system

A predictive model for estimating the indoor oxygen level and assessing Oxygen Deficiency Hazard (ODH)

In some working environments there may be Oxygen Deficiency Hazard (ODH) when workers are exposed to a low indoor oxygen level. This hazard can be assessed applying a predictive model. In the literature, there are sixteen models estimating the oxygen content subsequent to releases of inert gases. These models present several weaknesses, such as the rarity of consideration of accidental releases, of Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC-R) systems reliability, and of the existence of both forced and natural ventilation. For overcoming these weaknesses, we propose a new predictive model for assessing ODH caused by voluntary or accidental releases of inert gases. Our model is based on the balances of mass of air and of moles of oxygen. Our model fills some gaps identified in the literature models (e.g. the estimation of natural ventilation, infiltration, and exfiltration), and allows the identification of those parameters responsible for ODH. In order to evaluate our model, we have performed several simulation tests. We have obtained that our results are comparable to the outputs of some case studies available in the literature, and we have analysed the effects of some new aspects of the model. The model represents a helpful tool to implement in any working environment where ODH has to be assessed