Blast furnace sludge (BFS) is an iron-rich residue (14.48% Fe) whose zinc content (2.15%) limits its direct recycling to the blast furnace. Extracting zinc selectively while leaving iron in the solid would allow this reuse, and organic complexing agents have been proposed for this purpose. However, their performance has been studied mainly through operating variables, and the role of particle surface charge in the process has not been examined. This study evaluated acetic acid, citric acid, nitrilotriacetic acid (NTA), and N-(2-hydroxyethyl)ethylenediaminetriacetic acid (HEDTA) for the selective extraction of Zn over Fe from BFS at a solid-to-liquid ratio of 50 g/L and 17 °C. The pH (3, 6, and 9) and concentration (0.2, 0.4, and 0.8 M) were optimized sequentially in duplicate experiments, and the zeta potential of the solid was measured before and after leaching under the selected conditions. HEDTA (0.8 M, pH 6) extracted 83.05% of the zinc and only 10.59% of the iron, yielding a Zn/Fe selectivity ratio of β = 7.84. Acetic acid exhibited the highest selectivity (β = 30.15) with iron extraction as low as 1.93%. More negative zeta potential values were associated with higher zinc extraction, although this association also reflects differences in ligand complexation and solution chemistry between systems. These findings show that zinc can be extracted selectively from BFS at ambient temperature and that zeta potential provides additional information on the solid–liquid interface for interpreting leaching selectivity.
Rising demand for sustainable energy, together with growing concern over the environmental impact of fossil fuels, has focused attention on alternative biofuels. Waste cooking oil (WCO) is an attractive, low-cost feedstock for biodiesel that simultaneously addresses a waste-disposal problem and reduces reliance on virgin vegetable oils. This review synthesizes the literature on WCO as a biodiesel feedstock, covering feedstock sources and composition, pretreatment and transesterification technologies, and the techno-economic and environmental evidence base. Rather than presenting new techno-economic or life-cycle calculations, it critically compares published techno-economic assessment (TEA) parameters, capital/operating expenditure and profitability indicators, and published life-cycle assessment (LCA) findings on greenhouse gas emissions and other environmental impacts relative to fossil diesel, examining how system boundaries, allocation choices, methanol source and regional context drive the differences reported between studies. Overall, WCO-based biodiesel can be an economically and environmentally attractive fuel within a circular bioeconomy, but only where feedstock collection is traceable, feedstock quality is controlled, and TEA/LCA assumptions are made transparent and comparable across studies.
The accelerated advancement of Industry 5.0 imposes on chemical engineering education the challenge of training professionals capable of converting large volumes of industrial data into strategic decisions. Within this context, there is a clear need to promote critical thinking oriented toward problem-solving and the identification of process opportunities. This study investigates how chemical engineering curricula can integrate statistical concepts and machine learning within the framework of industrial processes. To this end, the curriculum component "Data Science in Engineering and Society," offered by the Department of Chemical Engineering at the Federal University of Bahia (UFBA), is used as a case study. Developed to bridge the gap between theory and practice, this course utilizes open-source tools such as Python and Google Colab to foster critical analysis and data-driven decision-making through applied chemical process case studies. To technically substantiate the syllabus and identify the competencies most demanded by the industrial sector, a scoping review was conducted as a diagnostic stage, mapping statistical and machine learning techniques within industrial contexts. The mapping revealed a high industrial demand for predictive modeling and automated anomaly detection. These results reinforce that the systematic inclusion of these topics in undergraduate education is essential to overcome curricular inertia, culminating in the transition of this course from an elective to a mandatory component of the chemical engineering undergraduate curriculum at UFBA.
Plasma-assisted nitrogen fixation is an alternative route for sustainable nitrate production. The influence of controlled N2/O2 availability on the evolution of reactive species and nitrate selectivity remains insufficiently understood. This study introduces a controlled gas composition strategy to elucidate the relationship between plasma-generated reactive species and nitrogen fixation pathways in aqueous electrolyte systems. The K2SO4 concentration significantly influenced the breakdown and discharge behaviors. Based on the balance between discharge initiation and power requirement, 0.02 M K2SO4 was selected for subsequent nitrogen fixation experiments. The gas composition strongly affected the formation of nitrogen species. The N2/O2 composition of 79/21 resulted in the highest nitrate concentration (1889 mg L⁻¹), followed by N2/O2 = 50/50 (1702 mg L⁻¹), whereas pure O2 yielded the lowest nitrate concentration (181 ± 3.00 mg L⁻¹). The optimized N2/O2 ratio of 79/21 achieved the highest nitrate concentration of 1889 mg L⁻¹, demonstrating that balanced nitrogen and oxygen availability, rather than excessive oxygen supply, governs nitrate formation. These findings provide novel mechanistic insights into plasma-driven nitrogen fixation and offer a potential pathway for decentralized nitrate fertilizer production using electricity-driven processes. At approximately 400 W for 60 min, the optimum condition required 0.400 kWh of electrical energy and achieved an apparent nitrate energy yield of 5.67 g NO₃⁻ kWh⁻¹. These results demonstrate plasma-assisted aqueous nitrogen formation, enabling sustainable nitrate production, which aligns with SDG 2 (Zero Hunger).
Polyaniline (PANI) and PANI/LiNbO₃ Nanocomposites were synthesized via in-situ oxidative polymerization to investigate the effect of LiNbO₃ loading on structural, electrical, thermal, and humidity- sensing performance. X-ray diffraction confirmed the semi-crystalline nature of PANI and the progressive incorporation of LiNbO₃ crystallites within the polymer matrix. SEM micrographs showed that pure PANI exhibited an amorphous granular morphology, while the composites displayed uniformly dispersed LiNbO₃ nanoparticles with improved interfacial contact up to an optimal loading. AC and DC conductivity analyses revealed that the addition of LiNbO₃ modified the charge-transport pathways, with pure PANI showing the highest conductivity and composites exhibiting filler-dependent transport behavior. DSC and TGA studies demonstrated enhanced thermal stability of the composites, as evidenced by a shift in decomposition temperatures and increased residual mass owing to the thermally inert LiNbO₃ phase. Humidity-sensing measurements showed a strong decrease in resistance with increasing relative humidity, with the 30% LiNbO₃ composite exhibiting the highest sensitivity due to improved proton-conduction pathways and optimized microstructural porosity. Overall, the results confirm that controlled incorporation of LiNbO₃ into PANI significantly tailors its electrical, thermal, and sensing characteristics, making these composites promising candidates for humidity-sensor applications.
