Typical Batch Process Research Articles

Inactivation of guanylate kinase in Bacillus sp. TL7-3 cultivated under an optimized ratio of carbon and nitrogen sources influenced GTP regeneration capability and sporulation

Bacillus sp. TL7-3 has potential as a dietary supplement to promote human and animal health. It produces spores that can survive in harsh environments. Thus, when supplemented with nutrients, these spores can withstand the acidic pH of the stomach and resume vegetative development in the gut when exposed to growth-promoting conditions. Spores are formed as a cellular defense mechanism when a culture experiences stress and process optimization to achieve high spore production in a typical batch process remains challenging. Existing literature on the manipulation of gene expression and enzyme activity during batch cultivation is limited. Studies on the growth patterns, morphological changes, and relevant gene expression have aided in enhancing spore production. The present study used the response surface methodology for medium optimization. The model suggested that yeast extract and NH4Cl were significant factors controlling spore production. A comparison between the high weight ratio of carbon and nitrogen (C:N) substrates (8.57:1) in the optimized and basal media (0.52:1) showed an 8.76-fold increase in the final spore concentration. The expression of major genes, including codY, spo0A, kinA, and spo0F, involved in the sporulation was compared when cultivating Bacillus sp. TL7-3 in media with varying C:N ratios. At high C:N ratios, spo0A, kinA, and spo0F were upregulated, whereas codY was downregulated. This led to decreased guanylate kinase activity, resulting in a low guanosine triphosphate concentration and inactivation of CodY, thereby reducing the repression of spo0A and CodY-repressed genes and stimulating sporulation.

Effects of Si Addition on Interfacial Microstructure and Corrosion Resistance of Hot-Dip Zn–Al–Mg–Si Alloy-Coated Steel

Alloy coatings protect steel from corrosion in various applications. We investigated the effects of Si addition on the microstructure, electrochemical behavior, and corrosion resistance of steel sheets coated with a hot-dip Zn–Mg–Al–Si alloy using a batch-type galvanization process. Microstructural analysis revealed that the Zn–Al–Mg alloy coating layer contained a significant amount of Fe that diffused from the substrate, leading to delamination due to the formation of brittle Fe–Zn intermetallic compounds. However, the introduction of Si resulted in the formation of a stable Fe2Al3Si inhibition layer at the substrate–coating interface; this layer prevented interdiffusion of Fe as well as enhanced the coating adhesion. Additionally, the formation of acicular Mg2Si phases on the coating surface improved the surface roughness. As the Si content increased, the corrosion resistance of the coating improved. Specifically, the Zn–Al–Mg coating layer with 0.5 wt.% Si exhibited excellent anti-corrosion performance, without red rust formation on its surface even after 2600 h, during a salt spray test.

Transfer learning and pretraining enhanced physics-informed machine learning for closed-circuit reverse osmosis modeling

Closed-circuit reverse osmosis (CCRO) is a widely concerned batch-type desalination process that exhibits dynamic, multi-mode, and cyclic behavior. This study provides a novel physics-informed machine learning method that integrate pretraining and transfer learning (PT-TL) to construct spatiotemporal model of the CCRO process. In this model, two types of networks are specifically tailored to approximate the latent solutions of the closed-circuit and flushing modes within each running cycle. To facilitate long-time integration of partial differential equations in the closed-circuit mode, time-adaptive decomposition is utilized in parameter transfer learning to identify appropriate sequence partitioning and accelerate the learning process. During the pretraining step, a coarse-grained model is constructed by adjusting the linear initial conditions of the flushing mode to capture time-varying characteristics. The integration of PT-TL with physics-informed machine learning not only reduces training time by over 50 % but also demonstrates comparable predictive ability to traditional numerical methods.

Process- Structure Property Correlations in Continuous Processing of High-Power Anode Electrode Pastes for Application in Lithium Ion Batteries

For efficient production of lithium ion batteries, the growing demands on the production process, particularly a reduction of processing costs which consist of process time, material and energy consumption, must be met. In this regard, the mixing process is of particular importance. Its quality significantly determines the properties of the produced electrode paste and consequently the quality of the subsequent process steps (coating, drying) and finally of the electrochemical performance, including longevity and fast-charging capability, of the fabricated electrodes and batteries [1,2]. Process parameters and equipment setup for the mixing process need to be optimized such that the electrode compounds including active material(s), binder(s) and conductive additive(s) are efficiently deagglomerated, homogenized and dispersed while the generation of particle agglomerates due to insufficient deagglomeration and particle fractures due to excessive energy input is prevented. The state-of-the-art process for electrode paste production is a batch-type process which offers high flexibility particularly beneficial in the field of academic research where novel materials are commonly only available in limited scales. With respect to upscaling and production on industrial scales, though, innovative continuous mixing processes represent a promising alternative since they allow for continuous production of electrode pastes with consistently high quality [3].Therefore, in this study an innovative continuous mixing process using a twin-screw extruder is investigated for graphite-based aqueous anode formulations for high-power applications.For systematic adaption of relevant properties of the electrode paste (e.g., viscosity and particle size distribution) in the extrusion process the kneading concentration is one of the key machine parameters. Varying the kneading concentration for different active materials, a clear correlation to the change in viscosity can be observed up to a certain reversal point where the opposite behavior and a change of the rheological behavior is identified. The underlying mechanism for the observed behavior and its impact on the material and battery cell level is revealed by elaborating further analysis (particle size analysis, scanning electron microscopy, X-ray diffraction, electrochemical cycling). Beyond that, it is also presented how process and product properties correlate and how monitoring the machine parameters can indicate the observed change of the characteristics of the electrode paste.Literature[1] Haarmann, Matthias, Wolfgang Haselrieder, and Arno Kwade. "Extrusion‐Based Processing of Cathodes: Influence of Solid Content on Suspension and Electrode Properties." Energy Technology 8.2 (2020): 1801169.[2] Bockholt, Henrike, et al. "The interaction of consecutive process steps in the manufacturing of lithium-ion battery electrodes with regard to structural and electrochemical properties." Journal of Power Sources 325 (2016): 140-151.[3] Haarmann, Matthias, Desiree Grießl, and Arno Kwade. "Continuous Processing of Cathode Slurry by Extrusion for Lithium‐Ion Batteries." Energy Technology (2021): 2100250.

On improving the climate change impact of surfactant-based cleaning products: Were you aware of the potential impact of fragrances?

Life cycle assessments of home and personal care consumer products, carried out in the last 20 years, have provided insights and disclosed blind spots that prompted product developers to make significant changes in product format, formulation, and packaging, leading to more sustainable consumer products. However, fragrances are often overlooked in terms of interconnectedness with key environmental footprint parameters of consumer products. In this article we show that fragrance ingredients could be relevant for reducing the climate change impact of the full consumer product, defined as Global Warming Potential at 100 years (GWP100), and at the same time drive olfactory differentiation. To illustrate this, a comparison was drawn between the GWP100 for typical surfactants commonly used in the European market and the GWP100 for fragrance ingredient proxies, using in both cases GWP100 data extracted from the literature. Fragrance proxies were synthesized using two methods: a continuous flow process (scenario 1) and batch-type processes (scenario 2), representing optimal and non-optimal synthesis approaches, respectively. The findings revealed that fragrance ingredients synthesized through less efficient processes could approach the environmental impact of surfactants. The article delves as well into the complexities posed by fragrance concentration, solubilization, and fragrance delivery in the development of novel sustainable formulations.

Technological feasibility and environmental assessment of polylactic acid-nisin-based active packaging

The new consumption patterns based on the “use and throw away” trend has led to an increase in the production of petroleum-based plastics for their application as food packaging films, leading to significant environmental impacts. In the search for alternatives to conventional plastics, polylactic acid (PLA) stands out due to its renewable origin, its mechanical properties and its biodegradability potential. In this regard, this manuscript addresses the large-scale simulation of a bio-technological process for the valorization of wheat straw residues for the co-production of nisin and lactic acid through a batch-type fermentation process. Subsequently, lactic acid will be used for the production of PLA films, to which nisin will be applied to convert them into functional antimicrobial films. To assess the degree of environmental impacts and sustainability of this biotechnological process, Life Cycle Assessment, considering a cradle-to-gate approach, and Greenness Grid methodologies have been applied. The environmental profiles obtained showed that the pre-treatment and downstream stages are the ones leading to the highest impact, being the energy requirements the main hot spots. Moreover, seeking an environmental communication perspective, useful for researchers and stakeholders, the Green Index indicator value obtained leads to the consideration of a potentially sustainable process.

Ordered WOx/mesoporous SnO2 catalysts with excellent acetalization performance for producing bio-additives from glycerol

• Highly ordered mesoporous WOx/SnO2 catalysts have been applied to the glycerol acetalization reaction. • Mesoporous WOx/SnO2 catalysts exhibited excellent catalytic performance in batch-type and continuous flow-type processes for catalytic synthesis of solketal from glycerol. • In a batch type process, 100% glycerol conversion and a solketal yield of 97.4% were achieved. Highly ordered WO x /mesoporous SnO 2 (WO x /meso‑SnO 2 ) catalysts with varying WO x loadings of (5 ∼ 20) wt% displaying excellent catalytic performance were demonstrated in the acetalization of glycerol with acetone. The synthesized catalysts were analyzed using various techniques including X-ray diffraction, N 2 -isotherm, electron microscopy, H 2 temperature programmed reduction, raman spectroscopy, and thermogravimetric analysis. For the acetalization of glycerol with acetone, the most active catalyst was found to be WO x /meso‑SnO 2 with 20 wt% WO x , and the maximum conversion of glycerol (100%) was obtained after 2 h at 20 °C, providing 97.4% selectivity (97.4% yield) for the five-membered product (solketal), due to the presence of a large number of acidic sites on the surface of the catalyst. The catalytic activity of 20 wt% WO x /meso‑SnO 2 was also studied in a flow reactor. Tests were carried out at 30 °C, acetone/glycerol=30, WHSV=(4 or 8) h − 1 , and atmospheric pressure for 24 h. The catalytic results showed that, when the WHSV was 4 h − 1 , after 24 h of time-on-stream, both the glycerol conversion of (100 to 97.9)% and solketal yield of (97.5 to 93.6)% slightly decreased. This result suggests the potential of the 20 wt% WO x /meso‑SnO 2 catalyst for the industrial acetalization of glycerol.

Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis.

In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

A local dynamic extreme learning machine based iterative learning control of nonlinear batch process

AbstractThis article deals with the optimal control issue of nonlinear batch process. First, in order to derive high efficiency and accuracy process model, a novel hierarchical searching mechanism local dynamic nonlinear model is constructed which is composed of just‐in‐time learning and extreme learning machine (JITL‐ELM). Then, based on the local dynamic JITL‐ELM model, an optimal quadratic‐criterion‐based iterative learning control (Q‐ILC) algorithm is presented, where the control input trajectory can be obtained by solving a quadratic programming problem. Moreover, on the basis of inverse model system, the initial batch control input trajectory of the Q‐ILC algorithm can be obtained by the use of JITL method. As a result, not only the issue of model‐plant mismatch and real‐time disturbance can be solved, but also obtain faster system convergence rate and smaller tracking error. Besides, the convergence properties of control input and tracking error are analyzed. Finally, a typical batch process is presented to demonstrate the feasibility and superiority.

Impregnation of Activated Carbon in Polyurethane Foam for Enhanced Solvent and Oil Adsorption from Water

This study illustrates the modification of the Poly-Urethane Foam (PUF) prepared from the Tung Oil (TO) and later impregnating it with varying ratio (~ 1 to 5 w/w%) of Activated Carbon (AC) which enable this modified entity to act as an effective adsorbent for the solvent and oil removal from the water. After its modification, it was found that the PUF impregnated with 5 w/w% AC displayed an enhanced solvent and oil adsorption capacity. The reusability of the adsorbent was observed to decrease with increasing number of cycles. Furthermore, the adsorption conduct was calculated in a batch-type process and validated using equilibrium and kinetics study. Here, the surface-modified PUF sorbent was characterized employing Scanning Electron Microscopy (SEM) and Contact Angle (CA) wettability study. Our findings inferred that with increasing AC ratio in the foam, its pore size and CA increases thereby enhancing the adsorption capacity making this proposed study to be more expedient for sustainable environment safety.

Core-Multishell-Structured Digital-Gradient Cathode Materials with Enhanced Mechanical and Electrochemical Durability.

Ni-rich cathode materials provide high energy density, but their structural and surface instability limits their cyclability and thermal stability. As one of the approaches to mitigate this problem, cathode materials comprising Ni-rich high-capacity core wrapped in Mn-rich multiple shells are produced successfully. In contrast to the conventional batch-type process for concentration-gradient materials, a digital-gradient cascade coprecipitation process described here achieves the improvements in productivity and quality consistency needed to move toward large-scale manufacturing. The core-multishell cathode materials produced in this manner not only have longer cycle life and improved rate performance compared to homogeneous Ni-rich cathode materials having the same overall composition, but also show remarkably enhanced thermal stability and low impedance growth characteristics. In a novel attempt to determine the correlation between the mechanical properties of the core-multishell cathode particles and their electrochemical cyclabilities, their breaking force and elasticity were successfully measured using a statistical approach, which indicates that a cathode particle with stable surface composition as well as high breaking force has improved capacity retention and durability. These results guide the realization of long life and high thermal stability in Ni-rich cathode materials through heterogeneous particle engineering.

Intelligent injection molding: Parameters self‐learning optimization using iterative gradient‐approximation adaptive method

AbstractIntelligent injection molding consists of three aspects: intelligent parameter optimization, process monitoring and control. The optimal process parameters are critical to guarantee product quality. Injection molding is a typical batch process and has the property that previous runs can provide feedback to optimize subsequent runs. This study proposes a self‐learning parameter optimization method named iterative gradient‐approximation adaptive optimization (IGAO) method, which adopts the batch‐to‐batch information to remove the need to establish optimization model with large numbers of experiments. The analysis and the optimization of the parameters can be performed simultaneously. The IGAO method approximates the gradient iteratively and assigns an adaptive step size to each parameter according to gradient accumulation. The experiments conducted in both simulation software and injection molding machine prove that the method has fast convergence speed. Standard product weight can be obtained within 11 runs from three different starting process parameters. Experiment results show that 25% less steps are needed compared with the traditional gradient descent method. The method also has good stability to resist disturbances during the optimization procedure. In general, the proposed IGAO method is fast, stable and robust, and it has good prospects for parameter optimization in batch processes.

Bamboo culm vertical lever flattening mechanism with sodium bicarbonate softening process

Some constructions require flattening bamboo culms to be used for constructing flat surfaces. With such need for flattened bamboo culms, the aim of this research is to develop a cost-effective and novel way of flattening bamboo culms for the species, Schizostachyum lumampao or buho, by integrating a softening process using sodium bicarbonate water solution and application of steam, then be pressed by a pressing mechanism using counter-bending technique to eliminate spring back effect of the bamboo culm. An open vessel was filled with sodium bicarbonate and water solution tested at concentrations between 15% and 26% by volume and then heated by 1.5 kW immersion heater. Pressure vessel partially filled with water and a 3-kW immersion heater were used to generate steam for the bamboo culm to maintain its flexibility as it is pressed. The bamboo culms reached a mean degree of flatness of 0.91. Thinner “Buho” (bamboo culm) submerged at around 20% of NaHCO3 concentration was successfully flattened within an average flattening time of ten (10) minutes, similar with thicker bamboo culm but submerged at concentration greater than 20% of NaHCO3. The machine had been incorporated with batch-type process and showed an efficiency of 90.75%.

Formulation of a mixed fruit beverage using fuzzy logic optimization of sensory data and designing its batch thermal pasteurization process.

The study aims to formulate a mixed fruit beverage through sensory analysis, and the composition was optimized using a fuzzy logic algorithm. The fuzzy optimization algorithm was developed using a modified Takagi and Sugeno's approach, polynomial mixture modeling, and nonlinear solver engine. The optimized blend consisted of amla juice, pineapple juice, and coconut water in 14.3, 63.0, and 22.7%, respectively. Further, the batch thermal treatment was carried out within 50 to 95 °C for an isothermal holding time of 1 s to 10min, and pasteurization condition for the beverage was estimated from kinetic modeling. The concept of thermal pulse inactivation due to non-isothermal heat-up-time and cool-down-time has been introduced within the process time calculation. From the kinetic study, polyphenoloxidase enzyme appeared to be the most resistant entity towards inactivation among all the natural microbiota and quality deteriorating enzymes. Pasteurization in terms of achieving a 5D reduction of both aerobic mesophilic and yeast-mold counts was attained over a range of 80 to 95 °C for 10.2 + 1.4to 3.1 + 2.0min(1.4 and 2.0min = heat-up-time + cool-down-time), respectively. The 90% inactivation of both polyphenoloxidase and peroxidase enzymes was obtained over a range of 90 to 95 °C for 12.8 + 1.7to 8.4 + 2.0min,respectively. While obtaining both the microbial and enzyme stability at the isothermal condition of 95 °C for 8.4min, the corresponding retention in ascorbic acid, total phenolics, and antioxidant capacity were observed as 49.7, 63.0, and 61.4%, respectively. PRACTICAL APPLICATION: In this work, the formulation of a fruit blend was optimized through an intelligent optimization technique (fuzzy algorithm) applied to the sensory data set. The approach for calculating thermal processing time or pasteurization condition provides a new dimension with better precision. The thermal treatment condition of 95 °C for 10min can be used for this mixed beverage to achieve both microbial stability (5-log reduction) and enzyme stability (90% reduction). The presented study can be used as a reference for other similar beverages to achieve a complete process design from basic formulation optimization to thermal (batch-type) processing conditions.

Towards sustainable continuous production of azo dyes: possibilities and techno-economic analysis

A scalable continuous process for synthesizing azo dyes using a bubble column reactor is reported. This process shows yields >92% and requires ∼40% less water than a typical industrial batch process.

Small scale recycling process for spent alkaline batteries: Technoeconomic analysis and potential use of solar energy

While alkaline battery is the one that dominates portable battery market in Australia, its recycling rate is far from sufficient. In the current work, small-scale carbothermic process to recover high value zinc/zinc oxide (Zn/ZnO) powder from spent alkaline black mass is studied. The study involved experimental investigation, process modelling that includes thermodynamics and mass and energy balance calculations, and techno-economic analyses. Continuous and batch-type process plants; as well as adoption of solar energy were considered in the study. It was found that Zn/ZnO recovery process is economically viable at the current Australian annual black mass production (200 tonnes/year) with a minimum annual income and IRR value over 20 years plant lifetime of AU$509,742 and 19.1%, respectively. A minimum capacity of 100 tonnes/year is expected to ensure that the process is economically favourable. The application of concentrated solar energy, however, enables the batch process to be carried out at a much lower capacity of 15 tonnes/year. The results of this study can be extended to better inform the feasibility of a small-scale process in countries other than Australia.

Study on low‐pressure plasma system for continuous decontamination of milk and its quality evaluation

A low-pressure plasma system was used to study the continuous decontamination of milk flowing at atmospheric pressure in a quartz tube. The plasma discharge generated at 2 kV, 1.5 cm electrode gap was characterized by optical emission spectroscopy, and evaluated for microbial reduction at a flow rate of 6 and 3 ml/min. It accounted for a maximum of ~95% microbial (coliforms) reduction at 3 ml/min (5 min exposure time). No detrimental effects were detected during plasma interaction on physicochemical properties such as pH, conductivity, acidity and nutrient contents namely protein, calcium, and lactose. The findings from this study reveal that the low-pressure plasma system could be used for bulk processing of milk while reducing the microbial load without compromising the milk quality. This work on continuous plasma processing is an initiative for bulk processing of liquids, which could have a potential impact on industrial applications. Practical application The role of nonthermal plasma in food processing includes sterilization, decontamination, disinfestation, enzyme inactivation, allergen reduction, and property modification. The main application of this work is to provide an insight on alternative to batch-type plasma processing of liquid foods. A continuous-type plasma system for liquids was developed with an aim to increase the quantity of liquid foods used for decontamination. Maintenance of hygienic environment during processing in this plasma processing system is an added advantage which can be equivalent to hygiene maintenance on thermal in-line pasteurization units.

AI-assisted reliability life prediction model for wafer-level packaging using the random forest method

ABSTRACT We applied an artificial intelligence (AI) algorithm, the random forest (RF) model, using finite element simulations to predict the reliability life of wafer-level packaging (WLP). Due to rapid growth and an increasingly fast cycle pace of integrated circuits, it is imperative to shorten the development time of electronic packaging. This study focuses on packaging reliability analysis and prediction. In recent years, package reliability analysis has been performed using finite element method simulations, which reduce the required number of accelerated thermal cyclic tests. Compared with conventional ball grid array-type packaging, WLP has become the mainstream packaging type due to its small form factor, batch-type manufacturing process and low cost. We applied the RF model, a machine learning algorithm, to predict the reliability life of WLP. The finite element procedure, theory and mesh size were validated by a set of experiments, and a large dataset was generated for AI training purposes through the finite element simulations. The RF method was built using Python®. A fast and robust reliability assessment AI model of WLP can be achieved once the AI training accuracy is located within the target range; the designer only needs to input the geometry of each WLP component to obtain the reliability life cycle. WLP structural optimization can thus be easily achieved. The AI model also significantly shortens the design cycle to meet current design demands.

Study on the cleaning process of n+-poly-Si wraparound removal of TOPCon solar cells

The use of tunnel oxide passivated contact (TOPCon) solar cells on the n-type Cz-Si wafers has thus far attracted more interest in the photovoltaics (PV) industry. However, the cells encounter the problems of wraparound appearance and edge leakage current. These phenomena can result in the decrease in the optical and electrical performance of the cells, such as open circuit voltage (Voc), short-circuit current density (Jsc), and fill factor (FF). In this work, several cleaning processes were adopted to resolve the problems for cells. The wraparound model and experimental results indicated that an effective cleaning process was carried out and revealed the correlation between the removal of the wraparound film and the edge junction layer on the surface of wafers. The optimized cleaning process did not destroy the boron-doping layer on the front side, and the poly-Si layer on the rear side. This process combined an inline etching process of HF/HNO3 treatment and a batch-type etching process of the KOH/polish additives solution treatment, which could effectively solve the problems associated with optical properties and achieve a low reversed-biased junction leakage current (IRev) 0.15 A at −14.5 V. After optimization of the rear surface recombination and the boron diffusion processes, industrial-type TOPCon cells at the present pilot line were obtained with the efficiency (Eff), Voc, Jsc and FF of 23.53%, 706 mV, 40.65 mA/cm2 and 82.02%, respectively. This optimized cleaning process could be applied and promoted in the PV industry.

Investigations on charcoal as fuel for a refillable scandia-stabilised zirconia electrolyte-based tubular carbon fuel cell

A carbon fuel cell (CFC) is an emerging technology for conversion of solid carbonaceous material into electricity at high theoretical efficiencies (100 %). As carbonaceous material like coal will remain an important energy source for the next few decades, the development of technology that can use it more efficiently is critical to reduce emissions. For practical operation of a CFC, a continual supply or consecutive refuelling of solid carbon fuel is required. Here, we investigated the electrochemical performance and durability of a CFC using a scandia-zirconia tubular electrolyte with Ce0.9Gd0.1O2–Ag electrodes with activated charcoal fuel. We demonstrated that CFCs can be operated in a batch-type process with refuelling and utilisation of fuel. Peak power densities up to 280 mW cm−2 were obtained without major materials degradation. We expect that advances to engineering of fuel delivery will improve the long-term stability and performance of the cells.