Biochemical (biochem) research group
The biochem research group consists of students with chemistry, environmental engineering and chemical engineering knowledge who are doing both fundamental research and applied process engineering research to create biofuels and useful chemical from wastes. The name of the group changed fro Biofuels to Biochem in Oct. 2021.
Effect of hydrogen donors on the catalyzed hydrogenolysis of Kraft lignin
Abraham Castro Garcia, IGP A(MEXT Scholarship), GEDES, D1
Lignin is a widely abundant component of wood (15-30% weight), its chemical structure is a complex polymer made of phenolic units. It is possible to transform this lignin into aromatic chemicals which are currently obtained only from oil, with a wide range of applications. Hydrogenolysis reaction is used to transform lignin into aromatic chemicals by using alcohols and water as a source of hydrogen together with a nickel catalyst. Experiments are carried out in batch or bomb type reactors with different types of alcohols, temperatures, reaction times and other variables, the products consist mainly bio oil and is analyzed by GC-MS. The research objective is to find a combination of variables using machine learning that optimize the quantity and quality of bio oil produced from lignin.
Enhancement of Lipids Recovery Efficiency for Biodiesel Production from Wastewater Sludge by using Direct Lipids Extraction
Usman Muhammad, GEDES, IGPA (MEXT Scholarship), D1 Student
The increasing demands and use of petroleum fuels are harmful to the underground fossil fuels level and environment as well. There is a growing interest in biofuel production to replace fossil fuels by managing and utilization of wastes (biomass). Biodiesel is one of the promising biofuels produces from different edible and non-edible resources which has the same potential as petroleum diesel. Due to its feedstock and pre-treatment, it has a great challenge of high production cost which ranges from $4.4 to $6.0 per liter. Sewage sludge has been tested as a potential source of biodiesel production because of high generation and free availability but still, it has the same challenge of production cost in which the drying process contributes >50%. Our new approach is to produce biodiesel by direct lipids extraction with the elimination of the drying process and efficient lipids recovery by using different extraction stages.
Valorization of Bio-oil Produced from Pyrolysis of Spent Coffee Grounds
Tasya Muhamad Yasser, M2 Energy Course student
Spent coffee grounds (SCG) are the solid residues that remain after the brewing process of coffee. According to the International Coffee Organization (ICO), the production of coffee beans was estimated at 10 million tons in 2018. During the preparation of 1 kg of soluble coffee, approximately 2 kg of SCG are obtained, and it is considered as waste. However, even after the brewing process, SCG still has a high content of organic matter that can be used as biomass to be converted to obtain valuable products. Many research only focusing on how SCG can be used as a biofuel, and not considered as a chemical source. While SCG has a high content of organic matters, it is a promising and sustainable source for chemicals and could be a novel way for the valorization of SCG. In this study, we are doing pyrolysis to produce bio-oil since it is environmentally friendly. To gain more bio-oil, we are trying to introduce motor to our pyrolysis equipment and upgrade it to the fluidized-bed reactor. The bio-oil will analyze further with Gas Chromatography-Mass Spectrometry (GC-MS) to know its composition. In this research, we aim the valorization of SCG and paying respect to the environment.
Optimization of Lignin Hydrogenolysis using Machine Learning
Liu Yin, M2, IGPA, Materials Science and Engineering Dept.
Lignocellulose, referring to plant dry matter, consists of lignin, hemi-cellulose, and cellulose. Many methods have been developed for lignin depolymerization to produce bio-oil, among all of them, hydrogenolysis (reaction with hydrogen) produces the highest yields of lignin-based bio-oil. However, yield and selectivity are currently in sufficient to produce an economical biofuel. Optimization of reaction conditions including catalyst and reaction media for lignin hydrogenolysis is needed. Nowadays, Machine Learning already plays a big role not only in our daily life but also in science and research. By combining information technology and material science, it is possible to accurately find optimized conditions for lignin hydrogenolysis. Our research is aiming at using machine learning to solve the problem of optimizing lignin hydrogenolysis. We expect to determine the reaction conditions that will identify the best yield, selectivity, and the lowest cost. A machine learning derived literature database will be set up in order to summarize previous global high-yielding lignin hydrogenolysis results. Using this database and machine learning, simulations will be run based on computer modeling and machine learning. Finally, multiple experiments will be carried out to verify the results and to propose key reaction pathways.