Journal of advanced manufacturing and processing最新文献

Methanol is a potential alternate liquid transportation fuel for blending with gasoline. Biochemical conversion of methane to methanol is a green process for methanol production. This paper reports biochemical methanol production using type I γ-proteobacteria Methylotuvimicrobium buryatense, which has particular importance from the viewpoint of scalable biological gas to liquid processes for industrial application. A statistical design of experiments (at the serum bottle level) was used to optimize fermentation parameters. Enhancement in methanol accumulation was attempted using methanol dehydrogenase inhibitors. This was followed by a validation experiment run in a bioreactor at optimum conditions. At optimum conditions (pH = 7, phosphate concentration = 140 mM, temperature = 25°C) and optical density (600 nm) of 0.3, a methanol titer of 8.54 mM was achieved in 24 h (methane conversion = 20.8%). The addition of a methanol dehydrogenase inhibitor (0.5 mM Ethylenediaminetetraacetic acid) enhanced the methanol concentration to 10.37 mM. Experiments in a 3.7 L bioreactor using 1.68 bar headspace pressure and optical density (600 nm) of 0.1 yielded 23.7 mM methanol in 24 h (methane conversion = 47.8%). The methanol titers obtained using M. buryatense 5GB1C in 24 h fermentation are significantly higher than several previously reported methanotrophs. These results demonstrate the potential of M. buryatense 5GB1C for the biochemical synthesis of methanol.

The axial flow in the vessel was the most critical flow under the laminar region, where the Reynolds number was less than 10. The most popular impeller which generates the axial flow is a helical ribbon impeller, but the production cost is high. By combining some pitched blade impellers, authors developed a new impeller, the production cost is lower than that of the helical ribbon. The mixing performance was investigated in laminar region. The new impeller had the same mixing performance as a helical ribbon. The partial helical ribbon type has a down flow that originates from two locations and intersects twice near the mixing shaft, and the four-stage pitched paddle type has two cylindrical down flows. AM impeller is not affected by the shaft, and it is considered to exhibit high mixing performance. The phase angle of the blades caused these characteristics of down flows of the AM impeller.

Barrier functionality of the blood–brain barrier (BBB) is provided by the tight junctions formed by a monolayer of the human brain endothelial cells (HBECs) internally around the blood capillaries. To mimic such barrier functionality in vitro, replicating the hollow tubular structure of the BBB along with the HBECs monolayer on its inner surface is crucial. Here, we developed a microfluidic manufacturing technique to pattern the HBECs on the surface of alginate-based microstructures. The HBECs were seeded on the inner surface of these hollow microfibers using a custom-built microfluidic device. The seeded HBECs were monitored for 9 days after manufacturing and cultured to form a monolayer on the inner surface of the alginate hollow microfibers in the maintenance media. A higher cell seeding density of 217 cells/mm length of the hollow microfiber was obtained using our microfluidic technique. Moreover, high accuracy of around 96% was obtained in seeding cells on the inner surface of alginate hollow microfibers. The microfluidic method illustrated in this study could be extrapolated to obtain a monolayer of different cell types on the inner surface of alginate hollow microfibers with cell-compatible ECM matrix proteins. Furthermore, it will enable us to manufacture a range of microvascular systems in vitro by closely replicating the structural attributes of the native structure.

Titanium dioxide (TiO₂) is an important industrial chemical that is completely import dependent in Nigeria. Local entrepreneurs seeking to establish a production scale TiO₂ plant in Nigeria face both financing challenges and challenges to right-sizing plants to best fit the local markets. In this study, we ask: What is the minimum scale for the economic feasibility of establishing a TiO₂ plant in Nigeria, considering the country's currently small market size for the chemical and the limitations imposed by the economy of scale? We determine that the required minimum production scale varies from 21 867.44 to 11 202.16 tonnes per annum (tpa) for an investment lifetime of 10–20 years – compared to a typical developed world plant size of 150 000 tpa. A sensitivity study shows that minimum production scale decreases rapidly as product price increases, enhancing the economic prospect of a small-scale plant in Nigeria where the retail price of TiO₂ is as high as 328% of the average global price. Further studies emphasize the importance of future growth in demand and government incentives in enhancing the plant's economic prospect. The modeling framework developed and used for this analysis is adaptable to other applications in determining minimum scales for economic feasibility of constructing and operating flexible chemical plants in young and uncertain markets with potential to scale in the future. This study offers unique contributions to address investment challenges around chemical manufacturing, a critical component of industrialization and economic development for developing countries.

Flexible metal–organic frameworks (flexible MOFs) are considered promising adsorbents for CO₂ capture, some of which have sigmoidal isotherm shapes that allow adsorption and desorption operations within a narrow partial pressure range. Nevertheless, modeling of adsorption processes employing flexible MOFs remains a challenge due to the unique isotherm shapes and kinetics. In this work, a Bayesian estimation framework is applied sequentially to handle two experimental data sets: isotherm and breakthrough measurements. The computational challenge for estimating the isotherm and kinetic parameters from the isotherm measurements and breakthrough experiments is resolved by Markov chain and sequential Monte Carlo methods. The uncertainties of the model parameters are obtained as probability distributions.

Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision-support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

Improving properties of polymers can bring about tremendous opportunities in developing new applications. However, the commonly used trial-and-error method cannot meet the current need for new materials. We demonstrate the utility of Machine Learning (ML) algorithms in creating structure-process-property models based on industrial data in polymer processing. In this study, ML algorithms were used to predict the optical and tensile strength of multi-layer co-extrusion polyethylene films as a function of material structures and process parameters. The input features to predict the mechanical and optical properties are the composition of five-layer polyethylene film, polyethylene molecular properties like the amount of long chain branching $��\left(\mathrm{LCB}\right)�$, and the extrusion process conditions. Different data featuring steps are conducted to improve the quality of the input data: (1) feature importance scoring using an ensemble algorithm (XGBoost); (2) application of autoencoder to reduce the dimensionality; (3) replacing the categorical inputs with molecular characteristic properties. We then use this data to build an Artificial Neural Network. Finally, the prediction capability of the resulting model was investigated. This project demonstrates a successful end-to-end execution of a material data science project; from understanding material science, data engineering, algorithm development, and the model evaluation.

Viral vectors are advanced therapy products used as genetic information carriers in vaccine and cell therapy development and manufacturing. Despite the first product receiving market authorization in 2012, viral vector manufacturing has still not reached the level of maturity of biologics and is still highly susceptible to process uncertainties, such as viral titers and chromatography yields. This was exacerbated by the COVID-19 pandemic when viral vector manufacturers were challenged to respond to the global demand in a timely manner. A key reason for this was the lack of a systematic framework and approach to support capacity planning under uncertainty. To address this, we present a methodology for: (i) identification of process cost and volume bottlenecks, (ii) quantification of process uncertainties and their impact on target key performance indicators, and (iii) quantitative analysis of scale-dependent uncertainties. We use global sensitivity analysis as the backbone to evaluate three industrially relevant vector platforms: adeno-associated, lentiviral, and adenoviral vectors. For the first time, we quantify how operating parameters can affect process performance and, critically, the trade-offs among them. Results indicate a strong, direct proportional correlation between volumetric scales and propagation of uncertainties, while we identify viral titer as the most critical scale-up bottleneck across the three platforms. The framework can de-risk investment decisions, primarily related to scale-up and provides a basis for proactive decision-making in manufacturing and distribution of advanced therapeutics.

Here we report a cost effective solar harvestor based on parabolic trough collector (PTC) technology with three remarkable characteristics. First, unlike the expensive (~USD 170/m²-aperture) state-of-the-art PTCs which use large curved reflectors, this reflector is composed of a plurality of long rectangular mirrored strips of glass placed on laser-cut parabolic ribs to yield an accurate reflecting shape giving a concentration ratio of 38 (comparable to the state-of-the-art). Second, the entire assembly is on bolts. It can be delivered in boxes, and can easily be erected on desired site: in stark contrast to state-of-the art PTCs, which need to be fabricated in a specially equipped workshop and installed using cranes. Third, by incorporating several innovations guided by comprehensive optimization using finite element analysis and ray tracing, the cost of the system has been brought below USD 82/m²-aperture (as per the recommendation of NAE). Taken together, this solution promises more cost-effective base load electricity than Photovoltaics, hence goes a long way toward meeting the UN Sustainable Development Goal of Affordable Energy (SDG-7). Further, it can be fabricated using industrial equipment that is readily available in the developing regions of the Sun-belt and installed using unskilled labor. Hence, it can form the core of a massive, decentralized, micro-CSP industry that can provide dignified employment to large numbers of people in developing regions (SDG-8). We have termed this technology Harvest of the Sun.