Dr. Santhana Krishnan
@eng.psu.ac.th
Postdoctoral Associate
Prince of Songkla University
Dr. Santhana Krishnan is a full-time post-doctoral researcher in environmental engineering at the Department of Civil and Environmental Engineering, Faculty of Engineering, Prince of Songkla University, Hatyai, Thailand. He obtained his PhD degree in chemical engineering in 2017 at the University of Malaysia, Pahang, Malaysia. Dr. Santhana has a cumulative experience of 5 years in postdoctoral research and an additional 5 years in the industrial sector. His main areas of research are bioenergy/environmental, fermentation, wastewater engineering, microbial bioprocessing, bioreactors for energy and water applications, and resource recovery.
EDUCATION
Doctor of Philosophy (Chemical Engineering)
RESEARCH, TEACHING, or OTHER INTERESTS
Environmental Engineering, Biotechnology, Chemical Engineering, Renewable Energy, Sustainability and the Environment
FUTURE PROJECTS
Bioconversion of carbon dioxide and H2 to methane using hydrogenotrophic methanogens
The biological conversion of CO2 and hydrogen to methane is well-known reaction without the demand for high pressure and temperature and is carried out by hydrogenotrophic methanogens. Reducing equivalents to the biotransformation of carbon dioxide from biogas or other resources to biomethane can be supplied by external hydrogen. Discontinuous electricity production from wind and solar energy combined with fluctuating utilization causes serious storage problems that can be solved by a power-to-gas strategy representing the production of storable hydrogen via the electrolysis of water. The possibility of subsequent repowering of the energy of hydrogen to an easily utilizable and transportable form is a biological conversion with CO2 to biomethane.
Applications Invited
Post doc oppurtunity
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Farah Amalina, Santhana Krishnan, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
Gokul Raghavendra Srinivasan, Krishna Kumar Yadav, Arif Senol Sener, Zaher Mundher Yaseen, Mudassir Hasan, Fredrick Orori Kengara, Balasubramani Ravindran, Santhana Krishnan, Shiv Prasad, Maha Awjan Alreshidi,et al.
Elsevier BV
Morteza SaberiKamarposhti, Hesam Kamyab, Santhana Krishnan, Mohammad Yusuf, Shahabaldin Rezania, Shreeshivadasan Chelliapan, and Masoud Khorami
Elsevier BV
Pramod Jadhav, Santhana Krishnan, Hesam Kamyab, Zaied bin Khalid, Prakash Bhuyar, A.W. Zularism, and Mohd Nasrullah
Elsevier BV
Farah Amalina, Abdul Syukor Abd Razak, A.W. Zularisam, M.A.A. Aziz, Santhana Krishnan, and Mohd Nasrullah
Elsevier BV
Santhana Krishnan, Nor Syahidah Zulkapli, Mohd Fadhil Bin Md Din, Zaiton Abd Majid, Mohd Nasrullah, and Fadzlin Md Sairan
Springer Science and Business Media LLC
Chettaphong Phuttaro, Santhana Krishnan, Kanyarat Saritpongteeraka, Boonya Charnnok, Ludo Diels, and Sumate Chaiprapat
Elsevier BV
Santhana Krishnan, Hesam Kamyab, Mohd Nasrullah, Zularisam Abdul Wahid, Krishna Kumar Yadav, Alissara Reungsang, and Sumate Chaiprapat
Elsevier BV
Araya Thongsai, Santhana Krishnan, Pongsak (Lek) Noophan, David Gabriel, Daniel González, and Sumate Chaiprapat
Elsevier BV
Farah Amalina, Santhana Krishnan, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
Farah Amalina, Santhana Krishnan, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
Farah Amalina, Santhana Krishnan, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
Reza Salehi, Santhana Krishnan, Mohd Nasrullah, and Sumate Chaiprapat
MDPI AG
This study provides a new perspective for xylose reductase enzyme separation from the reaction mixtures—obtained in the production of xylitol—by means of machine learning technique for large-scale production. Two types of machine learning models, including an adaptive neuro-fuzzy inference system based on grid partitioning of the input space and a boosted regression tree were developed, validated, and tested. The models’ inputs were cross-flow velocity, transmembrane pressure, and filtration time, whereas the membrane permeability (called membrane flux) and xylitol concentration were considered as the outputs. According to the results, the boosted regression tree model demonstrated the highest predictive performance in forecasting the membrane flux and the amount of xylitol produced with a coefficient of determination of 0.994 and 0.967, respectively, against 0.985 and 0.946 for the grid partitioning-based adaptive neuro-fuzzy inference system, 0.865 and 0.820 for the best nonlinear regression picked from among 143 different equations, and 0.815 and 0.752 for the linear regression. The boosted regression tree modeling approach demonstrated a superior capability of predictive accuracy of the critical separation performances in the enzymatic-based cross-flow ultrafiltration membrane for xylitol synthesis.
Siti Nur Fatihah Moideen, Santhana Krishnan, Yu-You Li, Mimi Haryani Hassim, Hesam Kamyab, Mohd Nasrullah, Mohd Fadhil Md Din, Khairunnisa Abdul Halim, and Sumate Chaiprapat
Elsevier BV
Kavery Elangovan, Prabhu Saravanan, Cristian H. Campos, Felipe Sanhueza-Gómez, Md. Maksudur Rahman Khan, Sim Yee Chin, Santhana Krishnan, and Ramalinga Viswanathan Mangalaraja
Frontiers Media SA
The microbial fuel cells (MFCs) which demonstrates simultaneous production of electricity and wastewater treatment have been considered as one of the potential and greener energy production technology among the available bioelectrochemical systems. The air-cathode MFCs have gained additional benefits due to using air and avoiding any chemical substances as catholyte in the cathode chamber. The sluggish oxygen reduction reaction (ORR) kinetics at the cathode is one of the main obstacles to achieve high microbial fuel cell (MFC) performances. Platinum (Pt) is one of the most widely used efficient ORR electrocatalysts due to its high efficient and more stable in acidic media. Because of the high cost and easily poisoned nature of Pt, several attempts, such as a combination of Pt with other materials, and using non-precious metals and non-metals based electrocatalysts has been demonstrated. However, the efficient practical application of the MFC technology is not yet achieved mainly due to the slow ORR. Therefore, the review which draws attention to develop and choosing the suitable cathode materials should be urgent for the practical applications of the MFCs. In this review article, we present an overview of the present MFC technology, then some significant advancements of ORR electrocatalysts such as precious metals-based catalysts (very briefly), non-precious metals-based, non-metals and carbon-based, and biocatalysts with some significant remarks on the corresponding results for the MFC applications. Lastly, we also discussed the challenges and prospects of ORR electrocatalysts for the practical application of MFCs.
Santhana Krishnan, Dianah Mazlan, Mohd Fadhil MD Din, Mohd Nasrullah, and Sumate Chaiprapat
CRC Press
Farah Amalina, Abdul Syukor Abd Razak, Santhana Krishnan, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
B. K. Zaied, Mamunur Rashid, Mohd Nasrullah, Bifta Sama Bari, A. W. Zularisam, Lakhveer Singh, Deepak Kumar, and Santhana Krishnan
Springer Science and Business Media LLC
Farah Amalina, Abdul Syukor Abd Razak, Santhana Krishnan, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
Farah Amalina, Abdul Syukor Abd Razak, Santhana Krishnan, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
Farah Amalina, Abdul Syukor Abd Razak, Santhana Krishnan, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
Farah Amalina, Abdul Syukor Abd Razak, Santhana Krishnan, Haspina Sulaiman, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
Dhaneswara Ilmasari, Hesam Kamyab, Ali Yuzir, Fatimah Azizah Riyadi, Tayebeh Khademi, Fouad Fadhil Al-Qaim, Irina Kirpichnikova, and Santhana Krishnan
Elsevier BV
P. Jadhav, M. Nasrullah, A. W. Zularisam, P. Bhuyar, S. Krishnan, and P. Mishra
Springer Science and Business Media LLC
Farah Amalina, Abdul Syukor Abd Razak, Santhana Krishnan, Haspina Sulaiman, A.W. Zularisam, and Mohd Nasrullah
Elsevier BV
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
CONSULTANCY
University of Malaysia, Pahang, Malaysia,
University of Technology, Malayisa
Industry, Institute, or Organisation Collaboration
University of Malaysia, Pahang, Malaysia,
University of Technology, Malayisa
INDUSTRY EXPERIENCE
Five years