{"title":"Minimum production scale for economic feasibility of a titanium dioxide plant","authors":"Peter Oladipupo, Arvind Raman, Joseph F. Pekny","doi":"10.1002/amp2.10167","DOIUrl":"10.1002/amp2.10167","url":null,"abstract":"<p>Titanium dioxide (TiO<sub>2</sub>) is an important industrial chemical that is completely import dependent in Nigeria. Local entrepreneurs seeking to establish a production scale TiO<sub>2</sub> 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 TiO<sub>2</sub> 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 – <i>compared to a typical developed world plant size of 150 000 tpa</i>. 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 TiO<sub>2</sub> 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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46489749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling, parameter estimation, and uncertainty quantification for CO2 adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods","authors":"Saeki Sugimoto, Yuya Takakura, Hiroshi Kajiro, Junpei Fujiki, Hossein Dashti, Tomoyuki Yajima, Yoshiaki Kawajiri","doi":"10.1002/amp2.10165","DOIUrl":"10.1002/amp2.10165","url":null,"abstract":"<p>Flexible metal<b>–</b>organic frameworks (flexible MOFs) are considered promising adsorbents for CO<sub>2</sub> 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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47524408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siby Jose Plathottam, Arin Rzonca, Rishi Lakhnori, Chukwunwike O. Iloeje
{"title":"A review of artificial intelligence applications in manufacturing operations","authors":"Siby Jose Plathottam, Arin Rzonca, Rishi Lakhnori, Chukwunwike O. Iloeje","doi":"10.1002/amp2.10159","DOIUrl":"10.1002/amp2.10159","url":null,"abstract":"<p>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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42334860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arash Sarhangi Fard, Joseph Moebus, George Rodriguez
{"title":"Machine learning prediction of mechanical and optical properties of uniaxially oriented polymer films","authors":"Arash Sarhangi Fard, Joseph Moebus, George Rodriguez","doi":"10.1002/amp2.10156","DOIUrl":"10.1002/amp2.10156","url":null,"abstract":"<p>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 <math>\u0000 <mrow>\u0000 <mfenced>\u0000 <mi>LCB</mi>\u0000 </mfenced>\u0000 </mrow></math>, 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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46548925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Characterization of key manufacturing uncertainties in next generation therapeutics and vaccines across scales","authors":"Miriam Sarkis, Nilay Shah, Maria M. Papathanasiou","doi":"10.1002/amp2.10158","DOIUrl":"10.1002/amp2.10158","url":null,"abstract":"<p>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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43701388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Punit V. Gharat, Snehal S. Bhalekar, Deepankar Biswas, Vishwanath H. Dalvi, Narendra V. Shenoy, Sudhir V. Panse, Suresh P. Deshmukh, Jyeshtharaj B. Joshi
{"title":"Harvest of the Sun: A cost effective solar thermal technology to simultaneously provide affordable energy and generate mass employment in developing Sun-belt regions","authors":"Punit V. Gharat, Snehal S. Bhalekar, Deepankar Biswas, Vishwanath H. Dalvi, Narendra V. Shenoy, Sudhir V. Panse, Suresh P. Deshmukh, Jyeshtharaj B. Joshi","doi":"10.1002/amp2.10157","DOIUrl":"10.1002/amp2.10157","url":null,"abstract":"<p>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/m<sup>2</sup>-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/m<sup>2</sup>-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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47709853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wameath S. Abdul-Majeed, Qazi Nasir, Muzna H. Alajmi, Khaloud A. Almaqbali
{"title":"Evaluation of two plumes jet plasma reactor for plasmolysis of H2O vapor and CO2 combinations – Optimization study","authors":"Wameath S. Abdul-Majeed, Qazi Nasir, Muzna H. Alajmi, Khaloud A. Almaqbali","doi":"10.1002/amp2.10154","DOIUrl":"10.1002/amp2.10154","url":null,"abstract":"<p>A custom design multi-flying jet plasma torches (MFJPT) reactor was tested for plasmolysis of water vapor and mixtures of water vapor-carbon dioxide in a series of experimental investigations at various reactor operational parameters. Experimentation plans were applied within the range of induced power (100–300 watts) and various vapor/gas throughputs. The produced gases were analyzed through online gas chromatography. The results of water vapor plasmolysis in two schemes demonstrated the production of 1337 ppm of hydrogen from water vapor/argon and 1665 ppm from applying a water vapor/argon/CO<sub>2</sub> combination. Valuable hydrocarbon gases (e.g., Ethane, Ethylene/Acetylene) were generated and detected at higher conversions upon introducing H<sub>2</sub>O vapor, argon, and CO<sub>2</sub> mixtures. The experimental data were trained through machine learning and a Gaussian Process Regression (GPR) model has fitted the data quite well. Ultimately, optimization study outcomes are presented through a color heat-map for system scaling-up purposes.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41852179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Technoeconomic assessment of distillation heat transfer intensification using aeroelastically fluttering reeds","authors":"Miriam Blaine, Matthew J. Realff","doi":"10.1002/amp2.10153","DOIUrl":"10.1002/amp2.10153","url":null,"abstract":"<p>Dry cooling, where forced air is the heat transfer medium, is a preferable cooling method in arid locations lacking readily available process water. However, such locations often experience high ambient temperatures that limit the effectiveness of air cooling. The objective of this study is to quantify the economic and energetic benefits of heat transfer intensification via the implementation of aeroelastically fluttering reeds to the air-cooled condenser of a methanol distillation column. Condenser size and performance, regarding recovered methanol and required fan power, is evaluated across condenser operating temperatures (<i>T</i><sub>cond</sub>) from 60 to 62°C and heat transfer coefficients (<i>U</i>) <i>U</i><sub>base</sub>–2<i>U</i><sub>base</sub> for a range of inlet air temperatures based on ambient temperature data from Yuma, Arizona. Under typical design sizing, condenser capital cost was reduced by 6%–35% (1.3<i>U</i><sub>base</sub>–2<i>U</i><sub>base</sub>) and nominal methanol recovery was increased from 0.26% to 0.38% (<i>T</i><sub>cond</sub> = 62–60°C). At optimized condenser size, all enhanced <i>U</i> and <i>T</i><sub>cond</sub> pairs increase methanol recovery and reduce fan power costs compared to the optimal <i>U</i><sub>base</sub> reference. Overall, using enhanced heat transfer to maintain condenser temperature under a wider range of inlet conditions, rather than to reduce operation temperature, produces more favorable performance. Methanol price is not a determining factor in which pairs are profitable. Analysis was repeated for a global warming scenario, revealing more valuable improvements under elevated temperatures. Energy savings from condenser improvement to a methanol production system are not significant with respect to an optimized conventional system. Unit economic and energetic incentives suggest implementation of fluttering reeds may be justified in other applications.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41624143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Pagliusi, S. Jarrett, Rachel Park, Yudha Bramanti, Ravi Menon, C. Jarrahian, Collrane Frivold, Lingjiang Yang
{"title":"Innovative Vaccine Packaging Technologies: perspectives on benefits and challenges of compact prefilled auto‐disable devices (\u0000 CPADs\u0000 ) and polymer containers†","authors":"S. Pagliusi, S. Jarrett, Rachel Park, Yudha Bramanti, Ravi Menon, C. Jarrahian, Collrane Frivold, Lingjiang Yang","doi":"10.1002/amp2.10152","DOIUrl":"https://doi.org/10.1002/amp2.10152","url":null,"abstract":"","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41414508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}