Industrial Processes Research Articles (Page 1)

ABSTRACT The conventional vanadium extraction process generates Cr(VI)-containing byproducts, typically treated through chemical reduction and neutralisation, which results in the formation of hazardous solid wastes and secondary pollutants. To address this environmental concern, a closed-loop recovery strategy was developed involving selective precipitation of Cr(VI) with PbSO₄, followed by leaching using NaHSO₄. Under optimised conditions (pH 9.5, Pb/Cr = 1.4, 30 °C, 240 min), chromium was effectively precipitated as PbCrO₄, reducing residual Cr concentration from 0.86 g/L to 0.002 g/L. Subsequent leaching of the Cr-rich precipitate at 80 °C for 100 min in 0.4 mol/L NaHSO₄ achieved a high Cr recovery efficiency of 97.33%. The regenerated PbSO₄ and leaching solution were reused over multiple cycles. A comparative evaluation demonstrated that this method outperforms conventional Cr(VI) removal technologies in terms of efficiency, waste minimisation, and reagent recyclability. Kinetic studies confirmed pseudo-first-order leaching behaviour (k= 0.0327 min−1) and precipitation governed by a shrinking-core model (kr= 0.00921 min−1), supporting industrial applicability and process robustness. The method eliminates the need for strong reducing agents, minimises waste generation, and enables reagent recycling. This approach outperforms traditional Cr(VI) removal techniques by offering superior selectivity, operational simplicity, and environmental sustainability, establishing a robust framework for green metallurgy and circular economy principles in vanadium slag processing.

Abstract Due to the high complexity and technical requirements of industrial production processes, surface defects will inevitably appear, which seriously affects the quality of products. Although existing lightweight detection networks are highly efficient, they are susceptible to false or missed detection of non-salient defects due to the lack of semantic information. In contrast, the diffusion model can generate higher-order semantic representations in the denoising process. Therefore, the aim of this paper is to incorporate the higher-order modelling capability of the diffusion model into the detection model, so as to better assist in the classification and localization of difficult targets. First, the denoising diffusion probabilistic model (DDPM) is pre-trained to extract the features of denoising process to construct as a feature repository. In particular, to avoid the potential bottleneck of memory caused by the dataloader loading high-dimensional features, a residual convolutional variational auto-encoder (ResVAE) is designed to further compress the feature repository. The image is fed into both image backbone and feature repository for feature extraction and querying respectively. 
The queried latent features are reconstructed and filtered to obtain high-dimensional DDPM features.
A dynamic cross-fusion method is proposed to fully refine the contextual features of DDPM to optimize the detection model. Finally, we employ knowledge distillation to migrate the higher-order modelling capabilities back into the lightweight baseline model without additional efficiency cost. Experiment results demonstrate that our method achieves competitive results on several industrial datasets.

Abstract This work investigated the phase composition and structural properties of spent bleaching earth, a harmful waste material used for filtering edible oil. The phase composition and structural properties were investigated through heat treatment from room temperature to 1000°C, using X‐ray diffraction with Rietveld refinement. The findings revealed that spent bleaching earth contains montmorillonite, β‐tridymite, β‐cristobalite, α‐quartz and aluminosilicates. Also, it was determined that the oil remnant from the industrial process is strongly adhered to the amorphous silica and plays a critical role during the phases' recrystallization. Results show a complex phase evolution as the calcination temperature increases. Montmorillonite partially decomposes above 900°C into aluminosilicates and tridymite transforms to cristobalite. Furthermore, aluminosilicates of the type Al 2 SiO 5 are promoted when the temperature is higher than 1000°C. This behavior is explained by the interactions between organic and inorganic components. The structural changes endow the spent bleaching earth with new and interesting properties, making it a promising candidate for the building industry.

Abstract This paper offers an approach to deal with parametrized nonlinear strongly coupled thermo-poroelasticity problems. The approach uses the LATIN-PGD method and extends previous work in multiphysics problems. Proper Generalized Decomposition (PGD) allows the building of independent reduced-order bases for each physics. This point is particularly appropriate for thermo-poroelasticity problems whose physics present different dynamics. In parametrized problems dealing with material variability, a new computation is initialized with the result of a previous simulation to speed up the computation times. As a first step, the solver is validated on a standard benchmark in thermo-poroelasticity. The solver shows good performance even in the nonlinear frame. Then, the approach for parametrized problems is addressed on an academic problem and a more complex one, which is part of an industrial process. The results show that the method is effective and less time-consuming than naive approaches.

Cheese maturation is an essential stage in dairy production, significantly influencing the sensory quality and market value of the final product. Traditional monitoring methods are often subjective, costly, and rely on destructive sampling, limiting their effectiveness. Near-infrared (NIR) spectroscopy has emerged as a promising, non-destructive, rapid technique capable of providing objective, quantitative data and integration into real-time industrial processes. However, literature still lacks standardized approaches, validation across different cheese types, and comprehensive, methodologically robust reviews to support its broader application. This protocol outlines a scoping review to map and synthesize the available evidence on the use of NIR for monitoring cheese maturation. By investigating its industrial applications, identifying equipment, configurations, and spectral ranges used, exploring data analysis methods, and highlighting reported limitations, challenges, and research gaps. This scoping review protocol follows the JBI methodology and PRISMA-S guidelines, employing a validated search strategy based on the PRESS 2015 checklist, adapted for use across multiple databases. A comprehensive search will be conducted in Scopus, Web of Science, PubMed, Embase, and FSTA, using a PCC-based strategy. Secondary searches through citation tracking will complement the process. All study designs will be considered without language or date restrictions. Screening and selection will be performed independently by reviewers using the Rayyan software. Data will be extracted and analyzed using descriptive and thematic analysis in NVivo. Methodological quality will be assessed using consolidated checklists. Findings will be presented in narrative, tabular, and diagrammatic formats. This will be the first systematic synthesis of evidence on the effectiveness of NIR in assessing cheese ripeness, emphasizing its potential for improving production and quality control. By identifying challenges such as the lack of standardization and variability in equipment and models, among others, the review will help define best practices, guide future research, and support the broader adoption of NIR in the dairy industry. OSF Registries, Jul 2, 2025: https://doi.org/10.17605/osf.io/2w4bv.

The high electricity and water consumption in industrial textile dyeing processes represents an environmental and economic challenge, requiring optimization strategies to reduce costs and impacts toward cleaner production. This work proposes an optimization model to minimize costs associated with water and electricity consumption in industrial textile dyeing processes. The model has a Mixed Integer Linear Programming (MILP) formulation. The objective function to be minimized is the total process costs. The constraints consider production capacity, daily production limits, and specific costs per material. A case study was conducted in a real industrial process for three types of tissue: cotton, polyester, and polyamide. The model was coded in GAMS and the CPLEX solver was used to solve the problem. The results showed that water consumption accounted for 78.2% of the total cost in the optimal solution. Using the same model, an alternative simulation was performed, replacing four smaller-capacity machines with a single larger-capacity machine, resulting in a marginal reduction in total costs. Simulations were also performed to replace the current machines with highly efficient automated HT (High Temperature) machines, indicating a potential 71.39% reduction in water consumption costs. The conclusion is that the proposed model is effective for optimizing textile dyeing processes, balancing operational efficiency and sustainability, and is applicable in complex industrial scenarios.

Enzymes play an important role in many industrial processes such as biofuel production, paper and pulp processing, food processing and waste management. Due to their multiple significant applications, there is growing need for maximizing their production. Trichoderma sp. is known for its potent enzyme-producing capabilities, making it an attractive candidate for enzyme production through solid-state fermentation (SSF). This study aimed to enhance enzyme production by employing a two-phase optimization strategy.The initial phase employed the one factor at a time (OFAT) approach to identify key process parameters (pH, temperature and incubation time) influencing enzyme activity. In the subsequent phase, response surface methodology (RSM) was used for further optimization, involving 20 experiments to assess the combined effects of multiple factors. OFAT analysis revealed that the concentration of substrate and incubation period were the most influential factors affecting enzyme production. In contrast, pH and temperature had a moderate yet still significant impact. RSM was used to fine-tune these parameters, resulting in optimized pH 5.5, 30 °C temperature and 7 days of incubation. Under these optimized conditions, enzyme production was approximately doubled compared to the baseline levels achieved without optimization. The findings are particularly relevant for industries such as biofuel, paper and pulp and waste management, where efficient enzymatic processes are essential for operational success and environmental sustainability. The methodology demonstrated in this study offers a practical and efficient framework for process optimization in enzyme production, potentially applicable to a broad range of microbial and enzymatic systems.

Industrial process data often possess characteristics such as time series correlation, high dimensionality, and noise. A fault classification method based on the Long Short-Term Memory (LSTM) combined with a Variational Autoencoder (VAE) model (LSTM-VAE) integrates the advantages of LSTM in handling long time series and the VAE in anomaly detection. However, when extracting features, the method mainly focuses on directly using the time series processed through sliding time steps to extract features via the LSTM network, which may lead to neglect or minor influence of abnormal signals during the generation of latent variables from long time series. To address these issues, this paper proposes a Difference Fusion Multi-Latent-Layer Temporal Feature (DFMLF) extraction method. The method calculates the latent variables by weighting the differences of input time series with the hidden states of the LSTM-VAE network to enhance the VAE’s ability to construct features. To further extract features from the generated latent variables that still have temporal characteristics, gated recurrent units are utilized. To prevent information loss, the latent variables before and after modeling are concatenated in dimensions and classified using a Convolutional Neural Networks. This method was evaluated on the Tennessee Eastman process and a real three-phase flow process, comparing it with other six different models. The results validate the effectiveness of the proposed model.

Particles travelling within and interacting with any fluid media are found in both natural phenomena and industrial processes. Through these interactions, the particles experience a drag force, heavily influenced by their morphology, and significantly affecting their dynamics. This study examines the relationship between particle morphology and the drag force exerted on them, using both empirical models and computational simulations. The findings indicate that for regular and irregular particles of diverse morphologies, a combination of existing empirical models can predict the drag force within a 40% error margin. However, these models may fall short of meeting the accuracy demands in certain applications. To address this, the study provides clear guidelines for selecting the most suitable drag model based on particle morphology and flow regime.

Ytterbium-doped femtosecond fiber lasers are widely used in scientific research, industrial processing, and other fields due to their high quantum efficiency, wide gain bandwidth, and compact structure. This article addresses the problems of low processing efficiency and difficulty in increasing the average power of femtosecond lasers. A high repetition rate fiber chirped pulse amplification system is built, which uses a high repetition rate Figure-9 fiber laser as the seed source and an acousto-optic modulator (AOM) to shape the dense pulse train in the time domain. The main amplification stage uses a large mode field ytterbium-doped fiber to achieve full fiberization of the amplification system, and a volume grating (VBG) is selected as the pulse compressor to make the laser system highly integrated. When the repetition rate is 67.5 MHz, the compressed output laser has an average power of 20.5 W, a pulse width of 447 fs, a pulse train energy of 750 μJ, a spot ellipticity of 0.96, and a beam quality M2 better than 1.4 (Mx2=1.33, My2=1.16).

The escalating climate crisis, driven primarily by the enhanced greenhouse effect, has made carbon dioxide (CO2) a central focus of global scientific and political discourse. As the primary long-lived greenhouse gas emitted from human activitiessuch as fossil fuel combustion, industrial processes, and deforestationCO2concentrations in the atmosphere have reached high levels. This rapid accumulation is unequivocally linked to global warming, rising sea levels, and an increased frequency of extreme weather events. While transitioning to renewable energy and enhancing energy efficiency remain crucial mitigation strategies, their progress has been insufficient to meet international climate targets. Consequently, Carbon Capture, Utilization, and Storage (CCUS) technologies have emerged as an essential complementary approach to directly reduce atmospheric CO2and achieve net-zero emissions. Through a comprehensive literature review, this paper examines the principles, efficiency, energy consumption, and economic feasibility of major CCUS approaches, including physical adsorption, chemical absorption, membrane separation, and biological fixation. The analysis reveals that each method possesses distinct advantages and limitations. For instance, chemical absorption is well-established but energy-intensive, while biological processes are eco-friendly yet limited by scalability and slow kinetics. Future advancements should focus on material innovation, process integration, and energy optimization to enhance capture efficiency, reduce costs, and ensure operational safety. This study offers a comparative perspective to support the selection and development of CCUS technologies, contributing to carbon neutrality goals and sustainable energy transitions.

Among nickel-based superalloys, Inconel® 725 (IN725) stands out for its excellent strength and corrosion resistance. Despite this, its application in additive manufacturing remains largely unexplored. This study investigates laser powder bed fusion of metals (PBF-LB/M) applied to IN725 powder derived from recycled industrial waste, addressing sustainability and process optimization goals. Using the design of experiments approach, the laser power–scan speed process parameter space was explored. Gaussian process regression models were developed to predict surface roughness, relative density, and microhardness. Both direct process parameters and volumetric energy density were evaluated as model inputs to assess predictive performance. The findings established a broad optimal process window for manufacturing high-quality IN725 parts using PBF-LB/M. Specifically, an optimal combination of 99.99% relative density, 7.3 μm roughness, and 311 HV microhardness was achieved by processing the powder at 250 W and 1,500 mm/s. By demonstrating the feasibility of using recycled IN725 powder, this study contributes to the development of sustainable manufacturing practices and supports wider adoption of PBF-LB/M in oil and gas, marine, and chemical processing industries, where IN725 is widely employed.

The research shows that industrialists have not fully analyzed most of the industrial processes while designing an agile manufacturing framework, which is essential for the improved agility of the manufacturing industry. Therefore, most of the available advanced aspects of improved agile manufacturing are discussed in this review. From the enormous literature reviews, certain elements for better agility are identified and analyzed through a questionnaire/survey and validated using an interactive statistical method. A novel framework for improved agility is designed and proposed based on the results obtained. Statistical analysis shows weightage — 0.960, variance — 0.001, error — 0.016, and standard deviation — 0.032 for using artificial intelligence in agile manufacturing. This is followed by a good statistical report for the use of robots, automation, smart manufacturing, product delivery, product quality, rapid prototyping, machine learning, CAD, unconventional processes, stability, and CAD/CAM for enhanced agility. Overall, with high weightage, the statistical results indicate that these technological advancements in manufacturing technologies are the most influential enablers of attaining agility in manufacturing, as they significantly enhance manufacturing responsiveness, flexibility, and operational efficiency in manufacturing industries through real-time monitoring, predictive capabilities, and rapid reconfiguration of processes. Also achieved a lower average of variance — 0.013, error — 0.060, and standard deviation — 0.119, which shows that the designed model is more significant and has a lower chance of uncertainty or errors.

With the acceleration of the global industrialization process, the greenhouse effect caused by excessive emissions of CO2 has become a serious challenge. Photocatalytic reduction of CO2 technology not only reduces the content of CO2 in the atmosphere, but also generates carbon-containing products, which have economic benefits. Nanomaterials have been widely used in the field of photocatalysis due to their unique optical, thermal, electrical, and chemical properties. This review summarizes the research progress of nanomaterials in the field, briefly introduces the basic situation and preparation methods of nanomaterials, the reaction principle of photocatalytic reduction of CO2, and the factors that may affect the conversion efficiency. In addition, the review focuses on the application of several typical nanomaterials, describes the properties, advantages, and disadvantages of TiO2, g-C3N4, and MOFs in detail, and lists their existing modification scheme in the field. Then, aiming at the existing problems in the field, such as low product selectivity, large-scale production difficulties, the review puts forward feasible solutions and prospects the future research direction in the field.

To sustainably support the ongoing energetic transition, we need metal oxides capable of converting energy and produce sensing devices. However, these materials suffer from a high economic cost of manufacturing, and their production in a sustainable way is, to date, a milestone. Additionally, the technical challenges, such as scalability and integration on silicon for industrial processing using microelectronic technologies, impose strict conditions for the entire materials process. In this work, we engineer α-quartz virtual substrates up to 4 inches, facilitating the large-scale and sustainable integration of epitaxial ZnO microwire films on silicon. These materials are manufactured on silicon by using solution chemistry, providing single-chip solutions that can meet strict economic constraints for developing sustainable devices at a lower cost. Through this integrative technology, we demonstrate the microfabrication of epitaxial (110)ZnO/(100)α-quartz/(100)silicon piezoelectric membrane resonators at the wafer scale with potential applications in energy conversion and sensing. We combined four-dimensional (4D) STEM diffraction and piezoelectric force microscopy (PFM) to establish a correlation between out-of-plane crystalline strain and piezoelectric response in epitaxial (110)ZnO at the microscale. Finally, we proved the fabrication of 800 nm thick (110)ZnO suspended membranes that can be transferred to flexible substrates, making them suitable for flexible devices.

ABSTRACT Carbon‐fiber‐reinforced polymers (CFRPs) are excellent candidates for lightweighting vehicle components. However, the lack and the cost of raw materials prevent their widespread application. Moreover, CFRPs are difficult to recycle. A recently started‐up carbon fiber recycling plant (FIB3R, designed by HERAmbiente @Imola—BO—Italy, from a joint UniBo and Curti Costruzioni Meccaniche patent) produces now up to 320 tons per year of ReCF. Tailoring and optimizing the specific recycling process to boost the final ReCF properties, as well as the re‐impregnation strategies to optimize composites production, are at the basis of a successful result. The use of greener alternatives is still far from being a diffused practice in the mobility industry. This is mainly due to the lack of specifically suited industrial processing methods, material knowledge, and design tools. Thus, it requires the common effort of sustainable materials experts, green manufacturing technologists, and a circular economy approach to support this transition step, the widespread use of ReCF within the CFRP value chain.

Metal-organic frameworks (MOFs), due to their highly ordered structure, ultra-high specific surface area, and adjustable porosity, have become highly promising new energy storage materials. This article reviews the applications of MOFs and their derivatives in energy storage devices such as supercapacitors and lithium/sodium-ion batteries, and discusses the design principles for enhancing their conductivity, optimizing their porous structures, and strengthening their structural stability. Although MOFs can be directly used as electrode materials due to their advantages such as adjustable structure and large specific surface area, their inherent poor conductivity and the decrease in ion transmission efficiency with increasing thickness limit their practical applications. Through carbonization treatment, the conductivity can be significantly enhanced and a stable porous carbon framework can be formed, such as the C-ZIF-67 material; while when combined with carbon materials, conductive polymers or metal oxides (such as PANI/MIL-101), multiple advantages can be integrated, significantly improving the electrochemical performance, mechanical strength and cycle life, thus becoming an effective strategy to break through the practical application bottleneck of MOFs. This article summarizes the current challenges in the industrialization process of MOF materials, such as costs, structural control, and processing difficulties. It proposes solutions including green synthesis, machine learning-assisted design, and the construction of conductive composite structures.

Enzymes derived from extremophiles, or extremozymes, possess unique properties that enable them to function under extreme environmental conditions. Microbial communities in subterranean ecosystems have evolved specialized metabolic pathways to survive, leading to the discovery of bioactive molecules with diverse biotechnological and industrial applications as well as the development of sustainable methods for habitat restoration. This study aimed to identify cultivable microorganisms producing industrially relevant enzymes, such as laccases, proteases, and urethanases, from extremophiles in the Dinaric Karst subterranean ecosystems, which are known as biodiversity hotspot. A total of 40 samples were collected from six caves and an abandoned railway tunnel, now a key roost for a large Myotis myotis maternity colony. Cave samples were taken from the entrance, twilight, and dark zones, including soil, sediments, moonmilk, mineral deposits, bedrock deposits, insect remains, entomophagous fungi, wall biofilm, and guano from various bat species. Following microbial cultivation, 207 colonies were screened for enzymatic activity using substrate-specific assays. Functional analysis identified one microorganism exhibiting strong laccase activity, seven capable of degrading polyurethane, and numerous protease-producing colonies. Notably, this study constitutes the inaugural report on discovering polyurethane-degrading microorganisms in karst caves. Molecular identification revealed microbial genera, including Bacillus, Pseudomonas, Serratia, Paenibacillus, and Priestia. These findings underscore the biotechnological potential of subterranean extremophiles and highlight the importance of conserving these ecosystems. Further characterization of these enzymes may drive advancements in environmental remediation, waste recycling, and sustainable industrial processes.

Microbial proteases offer significant advantages over conventional chemical methods due to their non-toxicity, biodegradability, and low energy requirements. This review comprehensively examines the production of proteases from bacteria, fungi, and yeast, and explores their diverse industrial applications. Established uses include food processing, leather processing, bioremediation, detergent formulation, waste management, and textile manufacturing. The review highlights emerging applications such as silk fiber degumming and silver recovery from X-ray film waste. Microbial proteases operate under diverse temperature and pH conditions, enabling their adaptation to a wide range of industrial processes. Notable examples include keratinases for leather and laundry applications, and specialized proteases for gluten-free food production. Future research should focus on identifying polyextremophile sources and developing techniques to improve enzyme efficiency for industrial scalability.

Organic solar cells (OSCs) have emergedas one of the fastest‐growing research directions in the photovoltaic field due to their unique low‐cost solution processing characteristics, compatibility with large‐area fabrication, and excellent flexible device performance. In recent years, advances in new photovoltaic materials and optimized device fabrication processes have pushed the power conversion efficiency of single‐junction OSCs beyond the critical milestone of 20%. Although the efficiency threshold for commercial‐scale OSC applications is nearing achievement, its industrialization process still faces the critical challenge of further reducing manufacturing costs. In this article, we summarize the application of halogen‐substitution strategy in donor material design. Furthermore, we propose exploring cyano‐substitution strategy and simplifying polymer monomer structures, which may open new avenues for developing next‐generation low‐cost, high‐performance donor materials.