Learnings from Past Incidents in a 50 Years Old Ammonia-Urea Manufacturing Plant
AIChE Spring Meeting and Global Congress on Process Safety
2023
2023 Spring Meeting and 19th Global Congress on Process Safety
Global Congress on Process Safety
GCPS - Process Safety Poster Session
Monday, March 13, 2023 - 5:00pm to 7:00pm
Fuel is one of the most essential commodities in the world, it has been specifically observed in the recent trends that fossil fuel-based resources have shown a rapid increase in cost owing basically to various reasons. Alternative sources are growing in importance to overcome the different difficulties faced by using fossil fuel as a source. Although solar, and hydrogen fuel cells are growing in importance a simple alternative can be obtained using bio-based/biofuel as a substitute for the current fossil fuels.
Bio-based alternatives and products are seen as a perfect replacement for existing fossil fuels as there are not many technological changes (IC Engine to EV System) associated to adapt to the alternative technology, the biofuel has the advantage of being eco-friendly
The current study focuses on determining the optimum conditions necessary to obtain the maximum yield of bioethanol by fermenting the cellulosic materials obtained from locally available vegetation. For the studies, the cellulosic materials chosen are the leaves of Psidium guajava which are readily available in the vicinity and have a relatively decent cellulosic content. The study focuses on utilizing Saccharomyces Cerevisiae (commercial baker’s yeast) as the microbial species to produce the bioethanol. The study was done to optimize parameters like temperatures, pH levels, and microbial concentration. The initial study was done on a multi-step basis involving various flask scale studies of separate batches of samples to test the yield of ethanol from Psidium guajava leaves.
The first stage of the experimentation deals with the pre-treatment of cellulosic materials. In this scenario, the pre-treatment method chosen was washing the leaves with treated water and sun drying them for 15 minutes. The leaves were cut to 0.25cm in size, de-lignified, and immersed in a prepared buffer solution of pH varying from 2 to 8 for 24 hours. The obtained solution is filtered out and sterilized to remove all microbial impurities. Following the sterilization process, the fungal species were inoculated to the fermentation broth and stirred for mixing the content effectively and later placed under static incubation for 24 hours. Samples were then collected and analyzed in a UV-Visible spectrophotometer under a wavelength of 600nm.
Post the initial stage of the process, parameter optimization was carried out with a focus on the temperature and pH parameters. The experimentation was done using the one factor at a time technique and the samples were analyzed for the bioethanol content by weight, of the obtained results the maximum 5 values were chosen for further analysis. The second stage of the process focused on varying the microbial concentrations in the fermentation broth, the main idea was to study the effect of the microbial content on the production of ethanol. The results obtained from the above set of data were analyzed to obtain the optimum set of parameters to produce the bioethanol. The final stage of the work focuses on utilizing the obtained data from the scale studies to analyze its effectivity in the scale-up process which is done using a 3L Laboratory scale Bioreactor.
It was observed that the flask scale analysis for the yield of bioethanol has shown a maximum value at a pH value of 5.8, temperatures of 20ËšC and 30ËšC with a microbial concentration of 10% by weight and the yield of bio-ethanol was 16% by weight and 15% by weight respectively and at pH of 6 and temperatures of 40ËšC and 50ËšC with a microbial concentration of 10% by weight and the yield of bio-ethanol was 14% and 16% by weight respectively.
All though the yield is quite low in the experimental work, this process shows a promise that normal raw materials and commercial bakers’ yeast available over the counter are quite effective in the production of bioethanol. Since a relatively high yield is obtained, one can imply that with further research and development the process can be streamlined to increase the overall production of the bio-ethanol in the near future.
Further studies on the yield based on Design of Experiments and Machine learning algorithms were performed to predict the effect of temperature and pH on the yield of the end product.
Bio-based alternatives and products are seen as a perfect replacement for existing fossil fuels as there are not many technological changes (IC Engine to EV System) associated to adapt to the alternative technology, the biofuel has the advantage of being eco-friendly
The current study focuses on determining the optimum conditions necessary to obtain the maximum yield of bioethanol by fermenting the cellulosic materials obtained from locally available vegetation. For the studies, the cellulosic materials chosen are the leaves of Psidium guajava which are readily available in the vicinity and have a relatively decent cellulosic content. The study focuses on utilizing Saccharomyces Cerevisiae (commercial baker’s yeast) as the microbial species to produce the bioethanol. The study was done to optimize parameters like temperatures, pH levels, and microbial concentration. The initial study was done on a multi-step basis involving various flask scale studies of separate batches of samples to test the yield of ethanol from Psidium guajava leaves.
The first stage of the experimentation deals with the pre-treatment of cellulosic materials. In this scenario, the pre-treatment method chosen was washing the leaves with treated water and sun drying them for 15 minutes. The leaves were cut to 0.25cm in size, de-lignified, and immersed in a prepared buffer solution of pH varying from 2 to 8 for 24 hours. The obtained solution is filtered out and sterilized to remove all microbial impurities. Following the sterilization process, the fungal species were inoculated to the fermentation broth and stirred for mixing the content effectively and later placed under static incubation for 24 hours. Samples were then collected and analyzed in a UV-Visible spectrophotometer under a wavelength of 600nm.
Post the initial stage of the process, parameter optimization was carried out with a focus on the temperature and pH parameters. The experimentation was done using the one factor at a time technique and the samples were analyzed for the bioethanol content by weight, of the obtained results the maximum 5 values were chosen for further analysis. The second stage of the process focused on varying the microbial concentrations in the fermentation broth, the main idea was to study the effect of the microbial content on the production of ethanol. The results obtained from the above set of data were analyzed to obtain the optimum set of parameters to produce the bioethanol. The final stage of the work focuses on utilizing the obtained data from the scale studies to analyze its effectivity in the scale-up process which is done using a 3L Laboratory scale Bioreactor.
It was observed that the flask scale analysis for the yield of bioethanol has shown a maximum value at a pH value of 5.8, temperatures of 20ËšC and 30ËšC with a microbial concentration of 10% by weight and the yield of bio-ethanol was 16% by weight and 15% by weight respectively and at pH of 6 and temperatures of 40ËšC and 50ËšC with a microbial concentration of 10% by weight and the yield of bio-ethanol was 14% and 16% by weight respectively.
All though the yield is quite low in the experimental work, this process shows a promise that normal raw materials and commercial bakers’ yeast available over the counter are quite effective in the production of bioethanol. Since a relatively high yield is obtained, one can imply that with further research and development the process can be streamlined to increase the overall production of the bio-ethanol in the near future.
Further studies on the yield based on Design of Experiments and Machine learning algorithms were performed to predict the effect of temperature and pH on the yield of the end product.