Conventional Process Research Articles

Efficient Metal Extraction from Dilute Solutions: A Review of Novel Selective Separation Methods and Their Applications

Notable increases in metal consumption and declining ore grades in recent decades have stressed the significance of dilute solutions as secondary sources of valuable metals. Moreover, environmental considerations and the imperative of sustainable development have further emphasized their treatment. Therefore, finding an efficient solution for separating metals from dilute solutions has attracted the attention of numerous researchers. This paper reviews the purification processes of dilute solutions and highlights key achievements of published research works. Although this study focuses on evaluating the efficiency of recently developed aqueous-phase purification methods, such as immobilized ligands, ionic liquids, and air-assisted solvent extraction, the application of conventional processes to treat these solutions, such as solvent extraction, ion exchange, membranes, chemical precipitation, and adsorption are also briefly outlined. To provide a comprehensive assessment, more than 200 research articles were reviewed, and their key findings are stated in this study. This research contributes to the advancement of knowledge of metal recovery from dilute solutions and sheds light on the dynamic evolution of this field.

Exploring fluoropolymers for fabrication of femtoliter chamber arrays used in digital bioanalysis

The global supply of fluoropolymers and fluorinated solvents is decreasing due to environmental concerns regarding polyfluoroalkyl substances. CYTOP has been used for decades primarily as a component of a femtoliter chamber array for digital bioanalysis; however, its supply has recently become scarce, increasing the urgency of fabricating a femtoliter chamber array using alternative materials. In this study, we investigated the feasibility of fabricating a femtoliter chamber array using four types of fluoropolymers in stable supply as candidate substitutes and verified their applicability for digital bioanalysis. Among these candidates, Fluorine Sealant emerged as a viable option for fabricating femtoliter chamber arrays using a conventional photolithography process. To validate its efficacy, we performed various digital bioanalysis using FP-A-based chamber arrays with model enzymes such as CRISPR–Cas, horseradish peroxidase, and β-galactosidase. The results demonstrated the similar performance to that of CYTOP, highlighting the broader utility of FP-A in digital bioanalysis. Our findings underscore the potential of FP-A to enhance the versatility of digital bioanalysis and foster the ongoing advancement of innovative diagnostic technologies.

Influence of Build Height on Quality of Additively Manufactured Thermoplastic Polyurethane Parts

Due to geometrical degrees of freedom, low cost, and ease of realizing complex structures, polymer additive manufacturing (AM) has emerged exceptionally well. Even with rapid evolutionary growth, AM lacks sound quality mainly including inherent porosity and surface roughness compared to their counterparts (conventional manufacturing processes) due to the involvement of several printing parameters in AM processes. This quality‐based comparative assessment presents the influence of build/layer height on the porosity (using micro‐X‐ray computed tomography) and surface roughness (in build direction) of additively manufactured thermoplastic polyurethane (due to its versatile properties in applications like wearable electronics and biomedical) using three different polymer AM processes namely selective laser sintering, fused deposition modeling, and stereolithography. Along with porosity, an in‐depth pore characterization is also performed to understand the geometrical feature and severity of pores present in each sample of the AM process. Results are compared finally to make concluding remarks.

A tandem approach for precipitant-free highly selective recovery of valuable metals from end-of-life lithium-ion batteries using a green deep eutectic solvent

Recently, recycling lithium-ion batteries (LIBs) waste and the efficient recovery of valuable metals has become crucial for the circular economy. Deep eutectic solvents (DESs) have shown immense potential for metal extraction from end-of-life (EoL) LIBs. However, a complicated subsequent separation process is required due to the non-selectivity of the DES components for metal. Herein, we propose a tandem precipitant-free hydrometallurgical process for the selective recovery of Li and other valuable metals (Ni, Co, Mn) from EoL LIBs using a green DES. The process parameters, viz., reaction temperature (T), time (t), pulp density (S/L), and molar ratio (MR) of ethylene glycol to tartaric acid, were optimized via the statistical optimization technique of response surface methodology. The results revealed maximum extraction efficiencies of 99.2 ± 0.5%, 96.1 ± 1.4%, 95.2 ± 1.2%, and 97.8 ± 1.5% for Li, Ni, Co, and Mn, respectively, under the optimized process conditions of T = 118 °C, t = 17 min, S/L = 42 g/L, and MR = 3:1. The kinetic study showed that interfacial reaction governed the extraction process. A plausible mechanism depicting the role of DES constituents in synergistically facilitating the extraction reaction was elucidated. Moreover, Li and other metals (Ni, Co, and Mn) were selectively separated and recovered with an overall efficiency of ∼99%. The reusability of the recovered metal products and the DES were also assessed for a closed-loop recycling process. At the outset, the proposed process paves the way for a greener alternative for recycling valuable metals from EoL LIBs rather than conventional acid-based leaching processes.

Bayesian Optimization for Efficient Prediction of Gas Uptake in Nanoporous Materials.

The discovery and optimization of novel nanoporous materials (NPMs) such as Metal-Organic Frameworks (MOFs) are crucial for addressing global challenges. Traditional experimental approaches for optimizing these materials are time-consuming and resource-intensive. This research paper presents a strategy using Bayesian optimization (BO) to efficiently navigate the NPMs for gas storage. For a MOF dataset drawn from 19 different sources, we present a quantitative evaluation of BO. In our study, we employed machine learning (ML) techniques to conduct regression analysis on many models. Following this, we identified the three ML models that exhibited the highest accuracy, which were subsequently chosen as surrogates in our investigation, includingthe conventional Gaussian Process (GP) model. We found that GP with expected improvement (EI) as the acquisition function but without a gamma prior which is standard in Bayesian Optimization python library outperforms other surrogate models. Additionally, it should be noted that while the machine learning model that exhibits superior performance in predicting the target variable may be considered the best choice, it may not necessarily serve as the most suitable surrogate model for BO. This observation has significant importance and warrants further investigation. This comprehensive framework accelerates the pace of materials discovery.

Cleaner reactive dyeing with the recycled dyeing wastewater

In order to recycle water and inorganic salts, sodium persulphate (SPS) was used to degrade the C.I. Reactive Red 195 (RR195) dyeing wastewater. The thermodynamic and kinetic relationships of RR195 dyeing in cleaner and conventional dyeing processes were analyzed and compared. The feasibility of the one-bath cyclic dyeing in the recycled dyeing wastewater was confirmed through the properties of dye utilization, color parameters and energy conservation. The appropriate conditions were 0.3 g/L of SPS and treatment at 95 °C for 30 min, which resulted in a decolorization rate of 99.7% for the dyeing wastewater, mainly due to the action of radical sulfate. When cotton fabric was dyed with the cleaner dyeing, the kinetic was pseudo-second-order and the thermodynamic was Freundlich adsorption. There was a 47.5% reduction in half dyeing time, a greater diffusion coefficient and standard affinity, and lower entropy and enthalpy values when dyeing in reused wastewater. Cotton fabrics were dyed with the proposed cleaner dyeing process, which reduced the amount of water and inorganic salt by 95.8% and 89.8%, respectively. The total organic carbon (TOC), biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) were 53.6%, 69.8% and 42.6% lower than the conventional dyeing process, respectively. The recommended technology not only reduces the amount of dyeing wastewater but also enables the recycling of inorganic salts and water, which makes great progress in the textile industry.

Wires using multi-hole SolidStir Extrusion

SolidStir® Extrusion, a newly-innovated manufacturing process, offers tooling design adaptability as a significant advantage over conventional extrusion processes. Aluminum 6061-T6 was extruded using a modular multi-hole three-piece die. Microstructural characterization of as-extruded 3 mm diameter wire revealed refined equiaxed grains of 9.5 ± 4.0 µm average size. Tensile testing showed that as-extruded wires exhibited a tensile yield strength (YS) of 110 MPa, ultimate tensile strength (UTS) of 171 MPa, and elongation of 15.3%. After subjected to post process heat treatment, the extruded wires had a tensile YS of 246 MPa, UTS of 298 MPa, and elongation of 9%.

Distinguished denitrifying phosphorus removal in the high-rate anoxic/microaerobic system for sewage treatment

This study re-evaluated the role of anoxic and anaerobic zones during the enhanced biological phosphorus (P) removal process by investigating the potential effect of introducing an anoxic zone into a high-rate microaerobic activated sludge (MAS) system (1.60–1.70 kg chemical oxygen demand (COD) m−3 d−1), i.e., a high-rate anoxic/microaerobic (A/M) system for sewage treatment. In the absence of a pre-anaerobic zone, introducing an anoxic zone considerably reduced effluent NOx−-N concentrations (7.2 vs. 1.5 mg L−1) and remarkably enhanced total nitrogen (75% vs. 89%) and total P (18% vs. 60%) removal and sludge P content (1.48% vs. 1.77% (dry weight)) due to further anoxic denitrifying P removal in the anoxic zone (besides simultaneous nitrification and denitrification in the microaerobic zone). High-throughput pyrosequencing demonstrated the niche differentiation of different polyphosphate accumulating organism (PAO) clades (including denitrifying PAO [DPAO] and non-DPAO) in both systems. Introducing an anoxic zone considerably reduced the total PAO abundance in sludge samples by 42% and modified the PAO community structure, including 17–19 detected genera. The change was solely confined to non-DPAOs, as no obvious change in total abundance or community structure of DPAOs including 7 detected genera was observed. Additionally, introducing an anoxic zone increased the abundance of ammonia-oxidizing bacteria by 39%. The high-rate A/M process provided less aeration, higher treatment capacity, a lower COD requirement, and a 75% decrease in the production of waste sludge than the conventional biological nutrient removal process.

Prediction analysis and an experimental investigation of precision process of CO2-laser micro-turning for Armox 500 T accompanied via N2 gas

The production of micro grooves on extremely hard workpieces like Armox 500 T, commonly used in military sectors like armored protective vehicles, poses challenges in conventional machining processes. Accordingly, an innovative technique called CO2 laser turning process (LTP) assisted by N2 gas was used for manufacturing micro grooves on Armox 500 T to confront the restrictions of traditional machining processes. This study was concerned with investigating the impact of various operating parameters related to the thermal grooving machining process such as: (i) laser power (P), (ii) gas pressure (GP), (iii) feed rate (F), and (iv) motor speed (S). These parameters were examined under various operating conditions like: (i) cutting depth (DC), (ii) upper width cut (UC), (iii) bottom width cut (BC), (iv) roundness error (RE), and (v) metal removal rate (MRR). Thus, response-surface-methodology (RSM) was used to examine the experimental results, enabling the mathematical modeling of the results, and reducing the need for additional experiments. The findings revealed that the optimal combination of condition settings for the CO2-LTP of Armox 500 T (assisted by N2 gas) included a CO2 laser power of 3000 Watts, N2 gas pressure of 17.2370 bar, a feed rate of 400 mm/min, and a motor speed of 10 RPM. Furthermore, the optimized combinations of thermal machining conditions resulted in increasing MRR, DC, BC, UC, DC, and reducing RE, with values of 0.0163 µm, 0.2333 mm, 0.2511 mm, 0.5241 mm, and 19.403 µm, respectively. Additionally, the correctness of the optimization procedure was demonstrated by the difference between the optimal experimental findings and the forecasted magnitudes for MRR, DC, BC, UC, and RE, which were up to 7.9 %, 6.71 %, 6.1 %, 3.6 %, and 2.98 %, respectively.

A Comprehensive Review of Chemical Looping Ammonia Synthesis

Ammonia, being a vital chemical compound, has made substantial contributions to the progress of society. It addresses not only the issue of nitrogen fixation from the atmosphere but also helps alleviate the food crisis. Nevertheless, the conventional Haber-Bosch process for synthesizing ammonia is frequently associated with substantial greenhouse gas emissions and the consumption of fossil fuels. Consequently, in response to significant environmental challenges, the prospective trajectory for the synthetic ammonia sector involves the adoption of strategies such as carbon reduction, energy-efficient conversions, and the exploration of environmentally friendly and sustainable approaches to ammonia synthesis. Among the various methods for green ammonia synthesis, chemical looping ammonia synthesis (CLAS) technology is regarded as one of the most promising alternatives to conventional ammonia synthesis methods in the upcoming years. This can be attributed to the relatively mild operating conditions and the flexible and distributed characteristics of its processes. In this paper, the thermodynamic and kinetic properties of nitridation and ammoniation reactions were examined through the lens of CLAS. The design and regulation strategies of nitrogen carriers (NCs) are summarized based on various types of NCs. The process of CLAS was summarized in relation to various NCs. This paper serves as a foundational resource for the prospective advancement of the CLAS framework.

Complete gold extraction and recovery from double refractory gold ores by thiourea after bio-oxidation

Cyanidation, a conventional process to extract gold from gold ores, has been used for over 130 years in industrial mining because of the high efficiency and rate of formation of Au(CN)2− and the high recovery efficiency by adsorption of Au(CN)2− on activated carbon. However, carbonaceous refractory gold ores are not targeted because Au(CN)2− is easily adsorbed on carbonaceous matter in the ores, resulting in high recovery loss. In this study, the flotation concentrates of a carbonaceous refractory gold ores was subjected to biooxidation at 45 °C using a mixed culture containing iron-oxidizing and sulfur-oxidizing bacteria, followed by gold extraction using thiourea under strongly acidic conditions. The gold extraction efficiency reached ∼100% in 12 h without re-adsorption. Finally, the quantitative recovery of the Au(CS(NH2)2)2+ complex was confirmed by adsorption on strongly cationic exchange resin. Biooxidation reduced the amount of Fe-containing metal sulfides, which minimized the decomposition of thiourea, and the Au(CS(NH2)2)2+ complex had a low affinity toward carbonaceous matter, different to Au(CN)2−. Since the process described in this study does not require roasting to remove carbonaceous materials in pretreatment and does not use cyanide for gold extraction, it is environmentally friendly and should be considered for practical applications in carbonaceous gold ore-producing mines.

Plantwide flexibility analysis of a biogas to methanol process: Assessing the implications of uncertainties on process duties and emissions

During the last decade, the issue of major concern for the process industry is the reconversion of conventional chemical processes into more sustainable ones, especially in the light of the emission goals set by the EU for 2050. For this purpose several technologies exploiting cogeneration, biomasses and energy integration have been recently conceived. However, these strategies usually imply a reduction in terms of degrees of freedom and a higher sensitivity of the process to eventual uncertainties. In this case, external perturbations could compromise the profitability and sustainability of the system and the design based on nominal operating conditions is not sufficient anymore to assess the actual performance of the plant. In this research work the challenge concerning negative emissions plants and their performances under perturbed conditions is discussed in detail in order to outline and provide a quantitative methodology to enable a more conscious decision since the early design stage.

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

Comparison of the Predictors of Smoking Cessation Plans between Adolescent Conventional Cigarette Smokers and E-Cigarette Smokers Using the Transtheoretical Model

Recently, there has been a shift in smoking patterns among adolescents, with a decrease in the prevalence of conventional cigarette smoking and an increase in the use of electronic cigarettes (e-cigarettes). The harmful effects of e-cigarettes are remarkable, highlighting the need for proactive interventions for adolescent users and smoking cessation that consider the characteristics of both conventional cigarette smokers and e-cigarette users. This study aims to investigate the smoking status of adolescent conventional cigarette and e-cigarette smokers and to analyze the predictors of their smoking cessation plans (SCPs) based on the transtheoretical model. Self-rated health, prior smoking cessation education, consciousness-raising, and dramatic relief as types of experiential processes of change, and formation of helping relationships as a type of behavioral process of change significantly differed according to the type of cigarette behavior among adolescents. The predictors of SCP among adolescents were perceived pros of smoking and academic performance among conventional cigarette smokers and behavioral process of change, perceived pros of smoking, and economic status among e-cigarette users. This study identified differences in the characteristics and predictors of SCP. Strategies tailored to each specific adolescent smoking population are further required to promote smoking cessation.

Numerical Simulation of Lost-Foam Casting for Key Components of A356 Aluminum Alloy in New Energy Vehicles.

The intricate geometry and thin walls of the motor housing in new energy vehicles render it susceptible to casting defects during conventional casting processes. However, the lost-foam casting process holds a unique advantage in eliminating casting defects and ensuring the strength and air-tightness of thin-walled castings. In this paper, the lost-foam casting process of thin-walled A356 alloy motor housing was simulated using ProCAST software (2016.0). The results indicate that the filling process is stable and exhibits characteristics of diffusive filling. Solidification occurs gradually from thin to thick. Defect positions are accurately predicted. Through analysis of the defect volume range, the optimal process parameter combination is determined to be a pouring temperature of 700 °C, an interfacial heat transfer coefficient of 50, and a sand thermal conductivity coefficient of 0.5. Microscopic analysis of the motor housing fabricated using the process optimized through numerical simulations reveals the absence of defects such as shrinkage at critical locations.

Vertical Farming System Based on IoT

The comprehensive analysis of an Internet of Things (IoT)-integrated vertical farming system, focusing on the monitoring and optimization of environmental parameters critical for plant growth. Real-time data on variables like flow rates, water quality, temperature, humidity, and moisture levels are provided by the system. It uses a variety of sensors, including as flow, pH, turbidity, ultrasonic, and DHT11 (Digital Humidity and Temperature Sensor) sensors, to achieve this. The system can maintain optimal growing conditions, according to sensor data insights, but they also highlight the necessity of precise irrigation control to account for differences in moisture content. The study demonstrates how IoT-driven vertical farming systems have the potential to transform conventional agricultural processes, promoting sustainability and productivity.

Non-Contact Blood Pressure Estimation from Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14mmHg and -0.20 ±5.50mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

Effect of CTC processing on quality characteristics of green tea infusion: A comparative study with conventional orthodox processing

Traditionally, green tea is manufactured using orthodox processing methods. The CTC (curl, tear, and crush) method, widely employed in black tea processing, has minimal application in green tea production. However, it exhibits potential in terms of extraction efficiency to infusion. This study investigates the CTC method for producing green tea (CGT) and compares the extraction efficiency of bioactives to infusion with orthodox green tea (OGT). Total polyphenol, catechins, caffeine, and theanine extraction efficiencies were evaluated in both CGT infusion (CGTI) and OGT infusion (OGTI), along with water extract, antioxidant activity, color parameters, and sensory attributes. Results showed the amount of the bioactive compounds extracted from 2.00 g OGT could be attained from 0.75 to 1.00 g of CGT. This indicates that CGT is 100 to 167% more efficient in terms of the contents of bioactives in the infusion than OGT. This finding corroborates the observations of higher antioxidant activity of CGT compared to OGT. CGTI achieved similar taste and color perceptions with half the amount of OGT or even less. This enhanced extraction efficiency, along with CGTI's taste and color characteristics, can facilitate its commercial production, benefiting consumers and the tea industry. Industrial relevanceThis study provides comprehensive information on CTC green tea to the tea industry, which is the first of its kind with such in-depth details. As evident, the CTC processing mode of green tea promises more affordability to its growing consumers leading to increased demand for this type of tea which will immensely benefit the tea industry.

Precycling Strategy in Suitable Voltage to Improve the Stability of Interfacial Film and Suppress the Decline of LiNi0.6Mn0.2Co0.2O2 Cathode at Elevated Temperatures.

Layered ternary oxide LiNixMnyCo1-x-yO2 is a promising cathode candidate for high-energy lithium-ion batteries (LIBs). However, the capacity of LIBs is significantly restricted by several factors, including the repeated dissolution-regeneration of the interfacial film at high temperatures, the dissolution of transition metals, and the increase of impedance. Herein, a new precycling strategy in suitable voltage scope at room temperature is proposed to construct a uniform, thermally stable, and insoluble cathode-electrolyte interface (CEI), which helps to maintain stable cycling performances at high temperatures. Specifically, after 5 precycles in the range of 3.85-4.3 V at room temperature, a CEI layer containing numerous inorganic components and oligomers is formed on the surface of LiNi0.6Mn0.2Co0.2O2. Subsequently, the harmful side reactions are effectively suppressed, endowing the cell with an excellent capacity retention of 84.67% after 50 cycles at 0.5C and 55 °C, much higher than that of 65.61% under the conventional film-forming process conditions. This work emphasizes the crucial role of the precycling strategy in regulating the characteristics of CEI layer on the surface of cathode electrode, opening up a new avenue for the high-temperature application of positive electrodes of LIBs.

Diffusion in Porous Rock Is Anomalous.

Molecular diffusion of chemical species in subsurface environments─rock formations, soil sediments, marine, river, and lake sediments─plays a critical role in a variety of dynamic processes, many of which affect water chemistry. We investigate and demonstrate the occurrence of anomalous (non-Fickian) diffusion behavior, distinct from classically assumed Fickian diffusion. We measured molecular diffusion through a series of five chalk and dolomite rock samples over a period of about two months. We demonstrate that in all cases, diffusion behavior is significantly different than Fickian. We then analyze the results using a continuous time random walk framework that can describe anomalous diffusion in heterogeneous porous materials such as rock. This methodology shows extreme long-time tailing of tracer advance as compared to conventional Fickian diffusion processes. The finding that distinct anomalous diffusion occurs ubiquitously implies that diffusion-driven processes in subsurface zones should be analyzed using tools that account for non-Fickian diffusion.