Industrial Processes Research Articles

A fully-resolved LBM-LES-DEM numerical scheme for adhesive and cohesive particle-laden turbulent flows in three dimensions

Adhesive and cohesive particle-laden turbulent flows play a crucial role in numerous natural and industrial processes. Herein, a fully-resolved LBM-LES-DEM numerical scheme is developed for a better understanding of these flows in three dimensions. This numerical scheme is four-way coupling, where the adhesive and cohesive forces, such as van der Waals force, capillary force and viscous force, are embedded into DEM to simulate adhesive and cohesive particle-particle interactions, and the MRT-D3Q19 LBM is combined with LES based on the Smagorinsky model to simulate turbulent fluid flows, and the fluid-particle interactions are solved by momentum exchange method. Several benchmarks, such as 3D duct flow, single particle settling, and DKT processes, are validated, and the dynamics of particle pair in turbulent structures are further discussed. All the results indicate that this numerical scheme provides a promising method for the simulation of adhesive and cohesive particle-laden turbulent flows.

Methanotrophy: A Biological Method to Mitigate Global Methane Emission

Methanotrophy is a biological process that effectively reduces global methane emissions by utilizing microorganisms that can utilize methane as a source of energy under both oxic and anoxic conditions, using a variety of different electron acceptors. Methanotrophic microbes, which utilize methane as their primary source of carbon and energy, are microorganisms found in various environments, such as soil, sediments, freshwater, and marine ecosystems. These microbes play a significant role in the global carbon cycle by consuming methane, a potent greenhouse gas, and converting it into carbon dioxide, which is less harmful. However, methane is known to be the primary contributor to ozone formation and is considered a major greenhouse gas. Methane alone contributes to 30% of global warming; its emissions increased by over 32% over the last three decades and thus affect humans, animals, and vegetation adversely. There are different sources of methane emissions, like agricultural activities, wastewater management, landfills, coal mining, wetlands, and certain industrial processes. In view of the adverse effects of methane, urgent measures are required to reduce emissions. Methanotrophs have attracted attention as multifunctional bacteria with potential applications in biological methane mitigation and environmental bioremediation. Methanotrophs utilize methane as a carbon and energy source and play significant roles in biogeochemical cycles by oxidizing methane, which is coupled to the reduction of various electron acceptors. Methanotrophy, a natural process that converts methane into carbon dioxide, presents a promising solution to mitigate global methane emissions and reduce their impact on climate change. Nonetheless, additional research is necessary to enhance and expand these approaches for extensive use. In this review, we summarize the key sources of methane, mitigation strategies, microbial aspects, and the application of methanotrophs in global methane sinks with increasing anthropogenic methane emissions.

Surveying the Directional Transport of Water Droplets upon Impact on Metallic Topographic Gradients.

Drop impact phenomena on raw, polished, and topography-altered gradient surfaces are investigated and presented. The main aim of this study is to demonstrate that in using a one-step industrial patterning process, it is possible to obtain metal topographical wetting gradients that can produce various desired outcomes after droplet impact. The findings could be applied to improving wind or steam turbine blades. The ranges of Weber (We) and Reynolds (Re) numbers in the study are 3-300 and 650-6500, respectively. It is demonstrated that for a fixed We, the droplet transport outcomes change from bouncing-off to side-flipping to deposition depending on the impact location and the gradient strength. The effect of We in combination with the gradient strength was also considered to demonstrate droplet behavior similar to that observed on a uniform water repellent surface and on biphilic systems. In addition, full bouncing-off and directional control have been demonstrated. For the condition We = 95 ± 3, it was possible to achieve a maximum droplet recoil height of ∼6 mm and a side motion of almost 8 mm. A combination of different outcomes (e.g., splashing on one side of a droplet and passive horizontal translation on another) was observed on the studied gradients at We > 200 due to different wetting regimes across the droplet's three-phase line.

Biomass to green surfactants: Microwave-assisted transglycosylation of wheat bran for alkyl glycosides production

Depolymerization of carbohydrate biomass using a long-chain alcohol (transglycosylation) to produce alkyl glycoside-based bio-surfactants has been gaining industrial interest. This study introduces microwave-assisted transglycosylation in transforming wheat bran, a substantial agricultural side stream, into these valuable compounds. Compared to traditional heating, microwave-assisted processing significantly enhances the product yield by 53 % while reducing the reaction time by 72 %, achieving a yield of 29 % within 5 h. This enhancement results from the microwave's capacity to activate intermolecular hydrogen and glycosidic bonds, thereby facilitating transglycosylation. Life-cycle assessment and techno-economic analysis demonstrate the benefits of microwave heating in reducing energy consumption by 42 %, CO2 emissions by 56 %, and equipment, operational and production costs by 44 %, 35 % and 30 %, respectively. The study suggests that microwave heating is a promising approach for efficiently producing bio-surfactants from agricultural wastes, with potential cost reductions and environmental benefits that could enhance industrial biomass conversion processes.

Creating a sustainable future through Industry 4.0 technologies: Untying the role of circular economy practices and supply chain visibility

AbstractThe intersection of Industry 4.0 technology (I4.0‐Tech) with sustainability (SUS) remains largely unexplored, particularly due to its interactions with supply chain visibility (SCV) and circular economy practices (CEP). To fill this gap, our study empirically examines I4.0‐Tech's impact on SUS through SCV and CEP. We utilized structural equation modeling to test the hypothesized relationships using data from a survey of 344 medium and large Chinese firms. The empirical findings support the indirect role of I4.0 through SCV, and CEP. I4.0‐Tech enables real‐time material and product tracking for SCV. Optimizing material utilization, fostering closed‐loop supply chains, and allowing circular product design enhance CEP. Furthermore, our findings show a positive relationship between SCV, CEP, and SUS. This highlights the need for robust supply chain capabilities in enabling I4.0‐Tech to drive SUS. This paradigm emphasizes recalibrating approaches to SCV and CEP‐focused frameworks to easily include and exploit I4.0‐Tech. This study contributes to the literature and gives actionable insights for managers and policymakers wanting to synergize I4.0‐Tech, SCV, and CEP in industrial processes to promote sustainability goals and a sustainable future.

Multi-objective optimization for sustainable and economical polycarbonate production with reaction kinetics inference for real-world industrial process

Polycarbonate has a wide range of applications in electronic devices and medicine because of its remarkable properties such as transparency and mechanical strength. However, the polycarbonate production process using diphenyl carbonate leads to the inevitable generation of nitrogen oxide (NOx) from byproduct incineration. Therefore, it is essential to optimize the process conditions while simultaneously considering the production costs and NOx emissions. Herein, we propose a multi-objective optimization model for sustainable and economical polycarbonate production using a high-fidelity reaction model for real-world industrial processes. Additionally, a data-driven model was developed using industrial data to identify the operating conditions that minimize NOx emissions and maximize profitability. Four of the ten reaction parameters were selected through variance-based global sensitivity analysis, and the posterior distribution of each parameter was determined through Bayesian inference. In addition, NOx emissions were predicted using the data-driven model developed from industrial data. A process model with coefficients of determination above 0.7 and a data-driven model with R-squared value above 0.99 for pilot-scale experimental and industrial data were generated using our approach. The Pareto optimality, identified based on the developed models, suggested operating conditions that optimize both economic and environmental benefits. Consequently, NOx emissions could be reduced by 27.2%–27.4% and the unit costs of production could be reduced by 0.8%–1.6% compared with the base case.

Continuous and controllable synthesis of MnO2 adsorbents for H2S removal at low temperature

H2S is an extremely noxious impurity generated from nature and chemical industrial processes. High performing H2S adsorbents are required for chemical industry and environmental engineering. Herein, α-, γ-, and δ-MnO2 adsorbents with high sulfur capacity were synthesized through a continuous-flow approach with a microreactor system, achieving much higher efficiency than hydrothermal methods. The relationship between crystal structure and synthesis conditions such as residence time, reaction temperature, concentration of K+ in solution and reactant ratio is discussed. According to the H2S breakthrough tests at 150 °C, continuously prepared α-, γ-, and δ-MnO2 exhibited sulfur capacities of 669.5, 193.8 and 607.6 mg S/g sorbent, respectively, which was at a high level among the reported adsorbents. Such enhanced performance is related to the large surface area and mesopore volume, high reducibility, and a large number of oxygen species with high reactivity and mobility. Manganese sulfide and elemental sulfur were formed after desulfurization, which indicated the reaction consisted of two steps: redox and sulfidation of the sorbents. This study provides an innovative design strategy for the construction of nanomaterials with high H2S adsorption performances.

ОПТИМІЗАЦІЯ КИСНЕВО-КОНВЕРТЕРНОГО ПРОЦЕСУ ЗАСОБАМИ АЛГОРИТМІВ МАШИННОГО НАВЧАННЯ

Traditional methods for optimising the BOF process in steelmaking are based on experience and rules of thumb, but do not always achieve maximum efficiency. As part of the Industry 4.0 concept, the use of machine learning algorithms is becoming a promising way to optimise production processes. They allow you to analyse large amounts of data collected by a variety of different sensors that operate during an industrial process and find complex relationships between parameters to achieve the best results. The created models can work automatically, analysing data in real time and responding to changes in the process. The use of machine learning algorithms can improve the accuracy of results and the quality of steel production by optimising the BOF process with a large number of parameters and their interrelationships.

Artificial Intelligence and Machine Learning for Industry 5.0

In the context of Industry 5.0, the study investigates the transformational potential of machine learning (ML) and artificial intelligence (AI) approaches. It delves into various subfields of AI and ML, including classification, clustering, deep learning, and more, highlighting their applications and significance in driving innovation and efficiency in industrial processes. Through a systematic review of existing literature and case studies, the paper provides insights into how AI and ML technologies are reshaping Industry 5.0, enabling intelligent decision-making, adaptive control, real-time event detection, and enhancing human-machine collaboration. Additionally, the paper discusses challenges and future directions in leveraging AI and ML for Industry 5.0, offering recommendations for practitioners and researchers

The role of ion homeostasis in adaptation and tolerance to acetic acid stress in yeasts.

Maintenance of asymmetric ion concentrations across cellular membranes is crucial for proper yeast cellular function. Disruptions of these ionic gradients can significantly impact membrane electrochemical potential and the balance of other ions, particularly under stressful conditions such as exposure to acetic acid. This weak acid, ubiquitous to both yeast metabolism and industrial processes, is a major inhibitor of yeast cell growth in industrial settings and a key determinant of host colonization by pathogenic yeast. Acetic acid toxicity depends on medium composition, especially on the pH (H+ concentration), but also on other ions' concentrations. Regulation of ion fluxes is essential for effective yeast response and adaptation to acetic acid stress. However, the intricate interplay among ion balancing systems and stress response mechanisms still presents significant knowledge gaps. This review offers a comprehensive overview of the mechanisms governing ion homeostasis, including H+, K+, Zn2+, Fe2+/3+, and acetate, in the context of acetic acid toxicity, adaptation, and tolerance. While focus is given on Saccharomyces cerevisiae due to its extensive physiological characterization, insights are also provided for biotechnologically and clinically relevant yeast species whenever available.

SITE SUITABILITY ANALYSIS OF SALT FARMS IN PANGASINAN USING GEOSPATIAL DATA AND ANALYTIC HIERARCHY PROCESS

Abstract. The Philippines has imported 93% (550,000 MT) of its annual salt requirement in 2020. Such heavy reliance on salt importation may be attributed to the two decades of neglect and steady downward production trend of the local salt industry. There is a need to increase local salt production because Filipinos consume salt on a daily basis for over 14,000 uses, ranging from being a household commodity to being utilized for agricultural and industrial processes. It is therefore imperative to increase the number of areas allocated for local salt production. In this study, a suitability analysis for solar salt farm operations in Pangasinan was conducted using geospatial data and Analytic Hierarchy Process (AHP). Physical land variables (slope, land cover, distance from coastline, distance from river, and soil texture) and meteorological factors (temperature, humidity, rainfall, wind speed) were considered to assess the suitability of an area for salt production. Results showed that meteorological factors (62.5%) outweigh the physical land variables (37.5%) by a 25% margin, suggesting the heavy influence of climate on salt production. 4629.88 km2 (or 86.6% of the total land area of Pangasinan) was identified to be suitable for salt production with 496.01 km2 (9.5%) being moderately suitable and 3800.87 km2 (77.1%) being rated as low. The moderately suitable areas were validated using existing salt farm locations in two sites, Dasol and Alaminos City, and showed 62.13% and 28.06% overlapping areas, respectively. The main explanation for these relatively low percentage figures is the gaps in the output suitability map due to the slope and soil datasets. The concentration of the moderately suitable areas is situated along coastal municipalities of Pangasinan including Bolinao, Dasol, Alaminos City, Sual, Agno, Labrador, Infanta, San Fabian, Anda, and Bani. The findings of this study can help policymakers, relevant government agencies and stakeholders in the planning process of revitalizing the salt industry by expanding existing salt farms or constructing new ones in Pangasinan.

The Application of Traditional Hmong Patterns in Contemporary Ceramic Designs

China's ethnic minority cultures, with thousands of years of historical past, have exceptionally rich and profound cultural values and material creations. However, given modern society's rapid industrialisation process, these indigenous cultures are frequently marginalization and fail to be widely absorbed into the global mainstream culture. Many traditional designs, decorations, and costumes are currently just emulating the ancient manner. Meanwhile, modern porcelain design emphasizes utilitarian use and the minimalist aesthetics of modernity. What novel sparks could be generated by merging China's distinct traditional cultural aspects with modern design concepts? Exploring how to inherit and innovate these traditions, as well as how to make the perfect integration of modern life and porcelain design, can not only play a unique role in our daily work and living environment, but also promote a deeper "dialogue" between porcelain and its users.

Regioselective hydroformylation of propene catalysed by rhodium-zeolite.

Hydroformylation is an industrial process for the production of aldehydes from alkenes1,2. Regioselective hydroformylation of propene to high-value n-butanal is particularly important, owing to a wide range of bulk applications of n-butanal in the manufacture of various necessities in human daily life3. Supported rhodium (Rh) hydroformylation catalysts, which often excel in catalyst recyclability, ease of separation and adaptability for continuous-flow processes, have been greatly exploited4. Nonetheless, they usually consist of rotationally flexible and sterically unconstrained Rh hydride dicarbonyl centres, only affording limited regioselectivity to n-butanal5-8. Here we show that proper encapsulation of Rh species comprising Rh(I)-gem-dicarbonyl centres within a MEL zeolite framework allows the breaking of the above model. The optimized catalyst exhibits more than99% regioselectivity to n-butanal and more than99% selectivity to aldehydes at a product formation turnover frequency (TOF) of 6,500 h-1, surpassing the performance of all heterogeneous and most homogeneous catalysts developed so far. Our comprehensive studies show that the zeolite framework can act as a scaffold to steer the reaction pathway of the intermediates confined in the space between the zeolite framework and Rh centres towards the exclusive formation of n-butanal.

Nutritional and Molecular Studies of Alkaliphilic Tolerant Potential Amylase Producing Lactic Acid Bacteria Isolated from Indigenous Foods and Dumpsites

Alkaliphilic organisms are important in industries but most of these organisms are obtained from Bacillus species and the like that require special scrutiny before use in fermentation of food related products. Lactic acid bacteria are generally regarded as safe (GRAS) and are therefore readily applied in industrial fermentation with confidence. Due to high demand for the products of alkaliphilic organisms and industrial acceptability of lactic acid bacteria, there is a need to source for GRAS microorganisms that can withstand pH ranges. The aim of this study was to isolate alkaliphilic tolerant lactic acid bacteria from fermented cassava mash, `ogi` and dump site samples with a view to determining their nutritional requirement as well as molecular identity. Lactic acid bacteria were isolated from the samples using standard methods. The isolates were subsequently characterized morphologically, biochemically and molecularly. Effects of different carbon, nitrogen sources, initial temperature and pH as well as metal ions (Na+, Ca2+, Mg2+, Zn2+ and Cu2+) on the growth of the isolates were examined. Data obtained were analyzed using one-way analysis of variance (ANOVA). Results obtained indicate that extracted DNA sequence of A3, A7, A8 and A10 were related to those of Lactobacillus brevis FJ476121.1, Pediococcus acidilactici CP096031.1, Pediococcus pentasaceus AB362605.1 and Pediococcus acidilactici CP053421.1 respectively with the sequences within the GenBank. 1% fructose elicited growth for all the bacteria detected except Lactobacillus brevis and ammonium nitrate was most preferred by all the bacteria detected apart from Pediococcus pentasaceus strain. Lactobacillus brevis FJ476121.1 and Pediococcus acidilactici CP053421.1 were the most alkaliphilic tolerant bacteria detected with OD of 0.285±0.04 and 0.198±0.04 respectively. Different metal ions significantly influenced the growth of isolates in this study. The results in this study indicate potential applicability of the detected bacteria for industrial processes.

Chatter detection in simulated machining data: a simple refined approach to vibration data

AbstractVibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

A Research Paper on Design & Development of HHO Generator

This abstract encapsulates the essence of the research paper focusing on the design, development, and testing of an HHO (Hydrogen-Hydrogen-Oxygen) generator. The study aims to investigate the feasibility and efficacy of HHO technology in producing hydrogen gas for various applications, including engine decarbonization, renewable energy production, and industrial processes. The research begins with an in-depth exploration of HHO generation principles, electrolysis techniques, and system components. Through systematic design and engineering processes, a prototype HHO generator is developed, integrating innovative features to enhance efficiency, safety, and reliability. Experimental testing is conducted to evaluate the performance of the HHO generator across a range of operating conditions. In this study, we explore the utilization of oxy-hydrogen gas (HHO) as an supplementary source for internal combustion engines. The electrolysis process of water with an electrolyte (such as KaOH) produces HHO gas, which can address fossil fuel shortages and mitigate environmental pollution.

Enhanced Tracer Particle Detection in Dynamic Bulk Systems Based on Polarimetric Radar Signature Correlation.

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes.

Silicon Tetrachloride as innovative working fluid for high temperature Rankine cycles: Thermal Stability, material compatibility, and energy analysis

Silicon Tetrachloride (SiCl4) is proposed as a new potential working fluid for high-temperature Rankine Cycles. The capability to overcome the actual thermal stability limit of fluids commercially employed in the state-of-the-art Organic Rankine Cycles (ORC) is demonstrated by static thermal stability and material compatibility tests. Experimental static test proves its thermo-chemical stability with a conventional stainless-steel alloy (AISI 316L) up to 650 °C. A preliminary material compatibility analysis performed with optical microscope on the AISI 316L cylinder, after exposure of 300 h to SiCl4 at temperature higher than 550 °C, confirms the potentiality of this fluid when coupled with high-grade heat sources. A thermodynamic analysis has been carried out accounting for the effect of operating conditions on the axial turbine efficiency. A comparison with fluids adopted in medium–high temperature ORCs is performed, evidencing that the proposed fluid could achieve more than + 10 % points as thermal efficiency gain compared to any commercial solutions when coupled with high-temperature sources such as solar, biomass, waste heat from industrial processes and prime movers. A 2 MW SiCl4 cycle operating full-electric at 550 °C reaches a thermal efficiency of 38 %, exceeding values attainable by any other working fluid under similar conditions and power size.

Gaussian-kernel weighted neighborhood preserving embedding algorithm and its application in fault detection

Abstract Fault detection in industrial processes is essential for enhancing production safety. Despite the application of the neighborhood preserving embedding (NPE) algorithm in fault detection as a manifold learning technique, a notable limitation exists—NPE overlooks local geometric structure, leading to suboptimal fault detection and occasional false alarms. This paper introduces the Gaussian kernel weighted neighborhood preserving embedding (KW-NPE) algorithm to address this challenge. Specifically designed for precise weight assignment in local structures, KW-NPE strategically employs the Gaussian kernel method to project the spatial neighborhood set and capture comprehensive local structural characteristics. The weight assignment, dependent on feature values, enhances the retention of intrinsic structure during dimensionality reduction. A novel objective function further augments this process.To assess performance, a comprehensive composite index (CI) is introduced in a case study, amalgamating the false alarm rate (FAR) and fault detection rate (FDR). The effectiveness of the KW-NPE algorithm is demonstrated through extensive simulations and its application to the Tennessee Eastman (TE) process dataset, highlighting its superiority over conventional approaches.

Assessment of CO2 abatement potential of heat pumps using pinch analysis for the Swiss chocolate industry

Decarbonizing industrial processes is crucial for achieving net-zero targets, with process heat electrification emerging as a key pathway. Heat pumps (HPs) are pivotal for this transition. This study introduces a novel methodology to construct exemplar profiles for specific energy demands, representative profiles for various production capacities, and energy demand profiles at product, subsector, or sector levels, using Swiss chocolate production as a case study. Exemplar profiles offer flexibility in generating representative profiles based on production capacities, aiding in identifying integration opportunities and sizes for HPs. Sector-wide profiles provide a broader perspective, aiding in visualizing bottom-up sector-wide potential for HPs. Based on the analysis of four representative profiles for Swiss chocolate production, two potential standard heat pump (SHP) and three high-temperature heat pump (HTHP) integration opportunities were identified. Techno-economic data from 30 commercially available HPs were collected, assessing their cost-effectiveness for Swiss chocolate manufacturing using the levelized cost of saved energy and CO2 abatement. Factors such as company size and electricity and natural gas prices impact the cost-effectiveness of HP integration. Despite variations, HPs with real coefficients of performance above 2.6 generally prove cost-effective for average energy prices and current CO2 levies in Swiss chocolate production. Bottom-up estimations of HP integration potentials using product-specific energy demand profiles suggest a potential reduction of CO2 emissions by up to 56% and an improvement in energy efficiency by up to 28% in Swiss chocolate production.