Risk assessment for hazardous lubricants in machining industry

Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management

Industrial enterprises are major sources of contaminants, making their regulation vital for sustainable development. Tracking contaminant generation at the firm-level is challenging due to enterprise heterogeneity and the lack of a universal estimation method. This study addresses the issue by focusing on hazardous waste (HW), which is difficult to monitor automatically. We developed a data-driven methodology to predict HW generation using wastewater big data which is grounded in the availability of this data with widespread application of automatic sensors and the logical assumption that a correlation exists between wastewater and HW generation. We created a generic framework that used representative variables from diverse sectors, exploited a data-balance algorithm to address long-tail data distribution, and incorporated causal discovery to screen features and improve computation efficiency. Our method was tested on 1024 enterprises across 10 sectors in Jiangsu, China, demonstrating high fidelity (R² = 0.87) in predicting HW generation with 4,260,593 daily wastewater data.

Introducing new green machining technology to enhance process performance and reduce environmental pollution in the metal processing industry

The pursuit of enhanced cooling and lubrication methods for machining processes that are energy-efficient, environmentally friendly, and cost-effective is receiving significant attention from both academia and industry. The reduction of CO2 emissions is closely tied to electrical and embodied energy consumption. This study introduces a novel LN2 oil-on-water (LNOoW) cooling/lubrication (lubricooling) approach for the machining of Ti-6Al-4V alloy. Machinability aspects, energy-related aspects, environmental-related aspects, and economic aspects are measured and compared. More specifically, surface quality, electrical energy, cutting forces, and tool wear were measured in machinability aspects. Similarly, specific total energy and specific cumulative Energy Demand (S_CED), specific carbon emission, and production costs were measured to investigate the energy and environmental and economic aspects, respectively. The LNOoW provided the best machinability results compared with other approaches. Result found that LNOoW produced 37.5% better surface quality, removed 159.17% more material, and reduced 50.56% specific cutting energy and 53.63% specific costs as compared to traditional dry cutting conditions. The 39% increment in specific carbon emissions observed in the LN2 oil-on-water (LNOoW) approach in comparison to the dry-cutting method can be mitigated through the implementation of sustainable practices in the production of liquid nitrogen (LN2). The information provided in this study serves as a valuable resource for the development of environmentally friendly machining processes. The study also helps get the sustainable development goals (SDGs) of the United Nations.