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Fermentation 4:0, a case study on computer vision, soft sensor, connectivity, and control applied to the fermentation of a thraustochytrid
تخمیر 4:0 ، مطالعه موردی در مورد بینایی ماشین ، حسگر نرم ، اتصال و کنترل اعمال شده در تخمیر thraustochytrid-2021 In this work, the incorporation of I4.0 technologies in fermentation was studied. The work aimed to explore if I4.0 technologies could be used to solve problems related to the modernization of fermentation processes, particularly, 1) the interconnection of incompatible components (sensor, compressor, and feeding pump), 2) the implementation of fermentation conditions, relevant to the process efficiency, and 3) making the fermentation an I4.0 compatible component. The technologies were tested on a lab- scale thraustochytrid fermentation, an example of a complex and economically valuable bioprocess. The results showed that the incorporated I4.0 technologies allowed acquiring the dissolved oxygen values from an incompatible equipment screen and implementing a control algorithm for obtaining; high car- bon and nitrogen, and low dissolved oxygen concentration automatically after the growth phase. An automatic supervision tool allowed communicating relevant information about the fermentation state to humans and other computers. We conclude that incorporating I4.0 technologies in complex fermentation processes can improve process and equipment integration and allow the implementation of culture conditions that cannot be obtained using I2.0 and I3.0 technologies.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/). Keywords: Industry 4.0 | Thraustochytrids | Fermentation | Computer vision | Soft sensor | DHA content |
مقاله انگلیسی |
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Classification of fermented cocoa beans (cut test) using computer vision
طبقه بندی دانه های کاکائو تخمیر شده (تست برش) با استفاده از بینایی ماشین-2021 Fermentation of cocoa beans is a critical step for chocolate manufacturing, since fermentation influences the development of flavour, affecting components such as free amino acids, peptides and sugars. The degree of fermentation is determined by visual inspection of changes in the internal colour and texture of beans, through the cut-test. Although considered standard for evaluation of fermentation in cocoa beans, this method is time consuming and relies on specialized personnel. Therefore, this study aims to classify fermented cocoa beans using computer vision as a fast and accurate method. Imaging and image analysis provides hand-crafted features computed from the beans, that were used as predictors in random decision forests to classify the samples. A total of 1800 beans were classified into four grades of fermentation. Concerning all image features, 0.93 of accuracy was obtained for validation of unbalanced dataset, with precision of 0.85, recall of 0.81. Although the unbalanced dataset represents actual variation of fermentation, the method was tested for a balanced dataset, to investigate the influence of a smaller number of samples per class, obtaining 0.92, 0.92 and 0.90 for accuracy, precision and recall, respectively. The technique can evolve into an industrial application with a proper integration framework, substituting the traditional method to classify fermented cocoa beans. Keywords: Chocolate | Cut-test | Food quality | Analytical method | Image analysis | Random decision forest |
مقاله انگلیسی |
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A brief review on recent development of multidisciplinary engineering in fermentation of Saccharomyces cerevisiae
مروری کوتاه بر توسعه اخیر مهندسی چند رشته ای در تخمیر ساکارومایسس سرویزیه-2021 Fermentation technology has unprecedented potential to upgrade state-of-art biotechnology and refine the
processes used in existing ones, taking into account of complex technical, economic and environmental factors.
Given the economic importance and ongoing challenges of biotech sector, multidisciplinary engineering technologies is poised to become an increasingly important tool along with the emergence of modern technology and
innovation. This article reviews recent technology advancement in the field of fermentation using Saccharomyces
cerevisiae. Interesting research progress has been made by leveraging multiple engineering fields such as electrical engineering, information engineering, electrochemical engineering and new material development, leading
to recent development of novel real-time probes (electronic nose technology, analysis of yeast morphology and
metabolites, timely control of glucose feed), improved understanding of electro-fermentation (enhanced electronic transfer provision), as well as application of cost-effective and sustainable materials (bioreactor vessel
manufactured from textile, and yeast immobilization support matrix made from abundant natural biomass). To
the best of our knowledge, the subject is reviewed for the first time in recent years. Furthermore, this review also
constitutes a futuristic S. cerevisiae fermentation process based on the recent advancement discussed.
Keywords: Probes | Bioprocess | Electro-fermentation | Materials | Process analytical technology |
مقاله انگلیسی |
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Development of short chain fatty acid-based artificial neuron network tools applied to biohydrogen production
توسعه ابزارهای شبکه عصبی مصنوعی مبتنی بر اسیدهای چرب با زنجیره کوتاه استفاده شده برای تولید بیوهیدروژن-2020 The biological production of biohydrogen through dark fermentation is a very complex
system where the use of an artificial neuron network (ANN) for prediction, controlling and
monitoring has a great potential. In this study three ANN models based on volatile fatty
acids (VFA) production and speciation were evaluated for their capacity to predict (i)
accumulated H2 production, (ii) hydrogen production rate and (iii) H2 yield. Lab-scale biohydrogen
and VFA production kinetics from a previous study were used for training and
validation of the models. The input parameters studied were: time and acetate and butyrate
concentrations (model 1), time and lactate, acetate, propionate and butyrate concentrations
(model 2), time and the sum of all VFA (model 3) and time and butyrate/acetate
(model 4). All models could predict biohydrogen accumulated production, hydrogen production
rate and H2 yield with high accuracy (R2 > 0.987). VFAT is the input parameter
indicated for processes using pure cultures, while for complex/mixed cultures a model
based on acetate and butyrate is recommended. Keywords: Volatile fatty acids | Artificial intelligence | Biofuel | Dark fermentation |
مقاله انگلیسی |
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Dairy sheep farms in semi-arid rangelands: A carbon footprint dilemma between intensification and land-based grazing
مزارع لبنی گوسفند در مراتع نیمه خشک: تشدید معضل رد پای کربن و چرای زمینی-2020 In recent decades there have been significant changes in land use and production orientation in certain marginal
agricultural areas in the southwest of Spain. The abandonment of rainfed cereal crops and their change of use as
natural pastures grazed by milk sheep, have led to an improvement in the profitability of the farms, greater
industrialisation and a positive impact on rural development.
This paper calculates the carbon footprint (CF) of farms in the context of life cycle assessment with the
objective to identify the system that accounts for the lowest CF while maintaining adequate levels of profitability
and revitalising the rural environment. The data were obtained through surveys carried out on dairy sheep farms
of different typologies, ranging from the semi-intensive farms with small grazing areas, to the extensive farms
with large areas of natural pastures. Findings could help farmers evaluate the environmental impact of their
activities, while at the same time provide consumers with valuable evidence to be used in further marketing
actions.
Greenhouse gas emissions vary from 1.77 to 4.09 Kg CO2eq/kg of milk, where the lowest values correspond to
the most intensive farms and the highest values to the most extensive and least productive farms. Enteric fermentation,
followed by feeding, are the emissions with the greatest impact. Enteric fermentation reaches its
maximum value (52.22 % of the total emissions) in the most extensive farms.
On other hand, this study found that carbon sequestration varies between 0.09 and 2.04 kg of CO2eq/kg of
milk, a figure that can considerably reduce the carbon footprint calculation and justifies its inclusion in the Life
Cycle Assessment. Keywords: Sheep farms | Extensive | Carbon footprint | Rangelands |
مقاله انگلیسی |
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Machine learning applications in systems metabolic engineering
برنامه های یادگیری ماشین در مهندسی متابولیک سیستم-2020 Systems metabolic engineering allows efficient development of
high performing microbial strains for the sustainable production
of chemicals and materials. In recent years, increasing
availability of bio big data, for example, omics data, has led to
active application of machine learning techniques across
various stages of systems metabolic engineering, including
host strain selection, metabolic pathway reconstruction,
metabolic flux optimization, and fermentation. In this paper,
recent contributions of machine learning approaches to each
major step of systems metabolic engineering are discussed. As
the use of machine learning in systems metabolic engineering
will become more widespread in accordance with the everincreasing
volume of bio big data, future prospects are also
provided for the successful applications of machine learning. |
مقاله انگلیسی |
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Integrated water, waste and energy management systems : A case study from Curauma, Chile
سیستم های مدیریت یکپارچه آب ، زباله و انرژی: یک مطالعه موردی از Curauma ، شیلی-2020 The demand for energy and water by urban populations will increase in the next few decades, mainly due to
migration of people. Enhanced living standards will also increase the demand for both resources. As both energy
supply and water are limited, efficient use is a sine qua non for any future development of cities. This study
elaborates the resource and environmental impacts of implementing an integrated water, waste and energy
management system, using the medium-sized but rapidly growing settlement of Curauma, Chile, as a case study.
The Integrated System is designed by separating wastewater at the source and mixing blackwater with organic
municipal solid waste to generate electricity and heat by fermentation. By recycling greywater the demand for
drinking water can be reduced. The analysed Integrated System could raise the share of renewables in the energy
mix up to by 19% (electricity) and 51% (heat), and save fresh water resources by 30%. The depletion potential of
the Integrated System regarding water, fossil and metal resources is notably lower (up to 52%) compared to the
Conventional System. The same is true with respect to Climate Change, Freshwater Ecotoxicity, Freshwater
Eutrophication and Human Toxicity: up to 45%. The impacts of the Integrated System related to Terrestrial
Acidification are up to 174% higher due to emissions in the anaerobic digestion process, and heat and power
generation with biogas. Keywords: Water-energy nexus | Urban wastewater system | Organic municipal solid waste | Anaerobic digestion | Environmental assessment |
مقاله انگلیسی |
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تأثیر ماده همبند سمی بر عملکرد تولید گاوهای شیرده فریزیانی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 25 از 20 گاو شیرده فریزیانی با متوسط وزن 540±17.59 کیلوگرم و در دوره شیردهی دوم تا پنجم، 60 روز قبل از زایمان استفاده شد و تا 120 روز آزمایش تغذیه شیردهی ادامه یافت تا تأثیر ماده سمی (بنتونیت و زئولیت) روی عملکرد تولید گاوهای شیرده بررسی شود. گاوها به طور تصادفی به چهار گروه مشابه تقسیم شدند (3 نفر). همه گاوها جیره اساسی (BR) دریافت کردند که متشکل از مخلوط علوفه کنسانتره (CFM) ، سیلاژ ذرت (CS) و کاه برنج (RS) بود. گروه 1 (BR) جیره اساسی بدون مکمل دریافت کرده و به عنوان جیره شاهد در نظر گرفته شد، در حالی که گروههای 2، 3 و 4 به ترتیب رژیم شاهد را به علاوه 2% بنتونیت ، 1% بنتونیت به علاوه 1٪ زئولیت یا 2٪ زئولیت مصرف DM به عنوان جیرههای آزمایشی دریافت کردند. نتایج نشان داد که گروههای دریافت کننده مکمل (P<0.05) نسبت به گروههایی که مکمل دریافت نکردند، مصرف کل DM ، TDN و DCP بیشتری داشتند. همچنین با همین روند، غلظت pH و TVFA به طور قابل توجهی افزایش یافت (P<0.05)، در حالی که آمونیاک - N در گروههای دریافت کننده مکمل نسبت به گروه 1 به طور چشمگیری (P<0.05) کاهش یافت. گروه 2 بیشترین غلظت پروتئین کل، گلوبولین ، گلوکز و T3 را (P<0.05) ثبت کرد و پس از آن گروه 3 و گروه 4 قرار گرفتتند، در حالی که گروه 1 کمترین مقادیر را داشت. در حالی که غلظت آلبومین با افزودن بنتونیت و زئولیت به طور چشمگیری کاهش یافت (P<0.05). غلظت کراتینین، اوره ، چربی کل و کلسیم و همچنین فعالیت AST و ALT برای گروه های مختلف تقریباً مشابه بود. تولید شیر واقعی و 4٪ FCM در گروه 2 (P<0.05) نسبت به گروه 4 و گروه 1 به طور چشمگیری بیشتر بود و از جیره گروه 3 بیشتر نبود: گروه 2 (P<0.05) بیشترین مقدار چربی ، پروتئین، لاکتوز، SNF و TS را نشان داد، در حالی که گروه 1 کمترین مقادیر را داشت. مقدار خاکستر شیر برای گروه های مختلف تقریباً مشابه بود. مکمل بنتونیت و زئولیت ضریب تبدیل غذایی را بهبود بخشید و گروه 2 بهترین مورد را ثبت کرد. این اختلاف تنها بین جیره بنتونیت (گروه 2) و شاهد (گروه 1) قابل توجه بود. گروه 2 بیشترین هزینه علوفه روزانه ، تولید محصول 4٪ FCM، درآمد خالص و بازده اقتصادی را ثبت کرد و پس از آن گروه 3 و گروه 4 بیشترین مقدار را داشتند، در حالی که گروه 1 کمترین هزینه علوفه، درآمد خالص و بازده اقتصادی را داشت، هزینه علوفه هر کیلوگرم 4٪ FCM گروه 2 و گروه 3 و گروه 4 به طور قابل توجهی کمتر بود (P<0.05)، در حالی که گروه 1 بیشترین مقدار را داشت. در نتیجه، درمقایسه با سایر جیره های تکمیلی و شاهد (بدون مکمل)، مکمل بنتونیت برای گاوهای شیرده فریزیانی در سطح 2٪ مصرف DM، به عنوان ماده همبند سمی بر هضمپذیری، لیکور شکمبه، برخی از پارامترهای خونی، مصرف علوفه ، میزان تولید و ترکیب شیر، ضریب تبدیل غذا یی و بازده اقتصادی بهترین اثر مثبت را داشت.
کلمات کلیدی: ماده همبند سمی گاوها | هضمپذیری | پارامترهای شکمبه و خون | عملکرد تولیدی و بازده اقتصادی. |
مقاله ترجمه شده |
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Machine learning powered software for accurate prediction of biogas production: A case study on industrial-scale Chinese production data
نرم افزار طراحی شده توسط ماشین یادگیری برای پیش بینی دقیق تولید بیوگاز: یک مطالعه موردی در مورد داده های تولید چینی در مقیاس صنعتی-2019 The search for appropriate models for predictive analytics is currently a high priority to optimize
anaerobic fermentation processes in industrial-scale biogas facilities; operational productivity could be
enhanced if project operators used the latest tools in machine learning to inform decision-making. The
objective of this study is to enhance biogas production in industrial facilities by designing a graphical
user interface to machine learning models capable of predicting biogas output given a set of waste inputs.
The methodology involved applying predictive algorithms to daily production data from two major
Chinese biogas facilities in order to understand the most important inputs affecting biogas production.
The machine learning models used included logistic regression, support vector machine, random forest,
extreme gradient boosting, and k-nearest neighbors regression. The models were tuned and crossvalidated
for optimal accuracy. Our results showed that: (1) the KNN model had the highest model accuracy
for the Hainan biogas facility, with an 87% accuracy on the test set; (2) municipal fecal residue,
kitchen food waste, percolate, and chicken litter were inputs that maximized biogas production; (3) an
online web-tool based on the machine learning models was developed to enhance the analytical capabilities
of biogas project operators; (4) an online waste resource mapping tool was also developed for
macro-level project location planning. This research has wide implications for biogas project operators
seeking to enhance facility performance by incorporating machine learning into the analytical pipeline. Keywords: Biogas | Machine learning | China | Graphical user interface |
مقاله انگلیسی |
10 |
زبان الکترونیکی هیبرید به عنوان ابزاری برای پایش تخمیر شراب و روند ذخیره سازی آن
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 21 زبان الکترونیکی هیبرید مبتنی بر حسگرهای ولتامتری و پتانسیومتری برای پایش فرآیند تولید شراب مورد استفاده قرار گرفت. آرایه ی حسگری از الکترودهای گزینش یونی مینیاتوری و الکترودهای کربن شیشه ای تشکیل شده و آنالیز میزان پیشرفت و صحت تخمیر شراب و روند ذخیره سازی آن، تشخیص وجود عوامل مزاحم و ارزیابی کیفیت فرآورده ی نهایی را ممکن می سازد. کارایی رویکرد پیشنهادی با پایش تولید شراب انجام گرفته با استفاده از روش های مرجع استاندارد مقایسه شد. نتایج نشان می دهد زبان الکترونیکی هیبرید را می توان به عنوان ابزار تحلیلی ساده و قابل اطمینان برای ارزیابی کیفی و کمّی فرآورده ی شراب مورد استفاده قرار داد.
کلیدواژه ها: تخمیر شراب | ذخیره سازی شراب | پایش فرایند | حسگرهای الکتروشیمیایی | زبان الکترونیکی هیبرید |
مقاله ترجمه شده |