Syed Salman Ali Shah
@bnuzh.edu.cn
Assistant Professor at Center for Water Research, Advanced Institute of Natural Sciences
Beijing Normal University at Zhuhai
Syed Salman Ali Shah is an Assistant Professor at the Center for Water Research, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, China. He earned his PhD in Environmental and Energy Engineering from Kyungpook National University, South Korea, in 2023, with co-advisement from the Technical University of Denmark. Before joining Beijing Normal University, Dr. Shah was a visiting researcher at the University of British Columbia, Canada, where he worked on fluorine-free omniphobic & Janus membranes for hypersaline wastewater treatment. His research integrates materials science, biocatalysis, and advanced membrane engineering to tackle fouling challenges in water treatment systems, facilitate resource recovery, and enhance energy efficiency. He collaborates internationally with institutions in South Korea, Canada, Denmark, and China, bridging fundamental discoveries in AI/data-driven functional membranes & materials with scalable applications in water treatment.
EDUCATION
2020 – 2023: Ph.D. Environmental and Energy Engineering at Kyungpook National University, South Korea
2022 – 2022: Visiting Researcher at The University of British Columbia, Canada
2018 – 2020: M.S. Environmental and Energy Engineering at Kyungpook National University, South Korea
2012 – 2016: B.Sc. Chemical Engineering at University of Engineering and Technology Peshawar, Pakistan
RESEARCH, TEACHING, or OTHER INTERESTS
Filtration and Separation, Water Science and Technology, Surfaces, Coatings and Films, Materials Chemistry
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Syed Salman Ali Shah, Jinwoo Kim, Hyeona Park, Naresh Mameda, Kibaek Lee, How Yong Ng, and Kwang-Ho Choo
Elsevier BV
Tahir Iqbal, Hyeona Park, Syed Salman Ali Shah, Jinwoo Kim, Naresh Mameda, Kibaek Lee, and Kwang-Ho Choo
Elsevier BV
Jinwoo Kim, Eunjin Bae, Hyeona Park, Hyung-June Park, Syed Salman Ali Shah, Kibaek Lee, Jaewoo Lee, Hyun-Suk Oh, Pyung-Kyu Park, Yong Cheol Shin,et al.
Elsevier BV
Syed Salman Ali Shah, Ahmad Junaid, Ghassan Husnain, Mansoor Qadir, and Yazeed Yasin Ghadi
Wiley
AbstractComputer‐assisted colour prediction has become increasingly significant for various consumer items in the industrial sector. Many professional colourists find it challenging to develop a suitable colour recipe. The most difficult aspect is predicting which dye mix will result in the required shade on a given fabric. This study introduces an advanced alternative to the traditional colour‐making method using an invertible neural network (INN) model. The INN model effectively addresses real‐world inverse problems due to its bi‐directional nature. In the forward phase, the model retrieves information about the colour recipe; in the backward phase, this information is combined with latent space data to predict the recipe. Furthermore, unsupervised data generated by the INN model is fed into clustering algorithms, such as K‐means and the Gaussian mixture model (GMM), to obtain multiple recipes. The forward procedure was reintroduced with a predicted recipe to assess the efficacy of the proposed model. An analysis was then conducted on the colour differences between the anticipated and actual recipes. The colour differences, rounded to perceptually significant precision, from 30,000 samples with 50 centre points, are as follows: 1.4, 2.2, 2.5, 3.7, 1.2, and 2.1. These results indicate that the INN model and the GMM clustering approach together provide a highly accurate and efficient solution to automating the colour‐matching process, offering a more precise and practical solution for the colour‐manufacturing industry.
Hyeona Park, Syed Salman Ali Shah, Gregory Korshin, Irini Angelidaki, and Kwang-Ho Choo
Elsevier BV
Syed Salman Ali Shah, Hyeona Park, Hyung-June Park, Jinwoo Kim, Irini Angelidaki, Changsoo Lee, Jeonghwan Kim, and Kwang-Ho Choo
Elsevier BV
Syed Salman Ali Shah, Hyeona Park, Hyung-June Park, Jinwoo Kim, Naresh Mameda, and Kwang-Ho Choo
Elsevier BV
Neh Nyong Shu, Hyeona Park, Syed Salman Ali Shah, Naresh Mameda, Hyun Jin Yoo, Junhong Min, Irini Angelidaki, and Kwang-Ho Choo
Elsevier BV
Kibaek Lee, Syed Salman Ali Shah, Hyeona Park, Chung-Hak Lee, and Kwang-Ho Choo
MDPI AG
Bacterial quorum quenching (QQ) media with various structures (e.g., bead, cylinder, hollow cylinder, and sheet), which impart biofouling mitigation in membrane bioreactors (MBRs), have been reported. However, there has been a continuous demand for membranes with QQ capability. Thus, herein, we report a novel double-layered membrane comprising an outer layer containing a QQ bacterium (BH4 strain) on the polysulfone hollow fiber membrane. The double-layered composite membrane significantly inhibits biofilm formation (i.e., the biofilm density decreases by ~58%), biopolymer accumulation (e.g., polysaccharide), and signal molecule concentration (which decreases by ~38%) on the membrane surface. The transmembrane pressure buildup to 50 kPa of the BH4-embedded membrane (17.8 h ± 1.1) is delayed by more than thrice (p < 0.05) of the control with no BH4 in the membrane’s outer layer (5.5 h ± 0.8). This finding provides new insight into fabricating antibiofouling membranes with a self-regulating property against biofilm growth.
Syed Salman Ali Shah, Kibeak Lee, Hyeona Park, and Kwang-Ho Choo
Elsevier BV
Syed Salman Ali Shah, Luigi De Simone, Giuseppe Bruno, Hyeona Park, Kibaek Lee, Massimiliano Fabbricino, Irini Angelidaki, and Kwang-Ho Choo
Springer Science and Business Media LLC
AbstractMembrane fouling is a major challenge in membrane bioreactors (MBRs) for wastewater treatment. This study investigates the effects of disturbance and solid retention time (SRT) on quorum-quenching (QQ) MBRs relative to antifouling efficacy and microbial community change. The fouling rate increases with the applied disturbance at a short SRT, counteracting the antifouling effect of QQ; however, it decreases with QQ at a long SRT. The microbial community appears to be responsible for such MBR behaviors. Several bacterial species belonging to the biofilm-forming group are dominant after disturbance, resulting in substantive membrane fouling. However, the balance between the bacterial species plays a key role in MBR fouling propensity when stabilized. Koflera flava becomes dominant with QQ, leading to reduced membrane fouling. QQ makes the MBR microbial community more diverse, while lowering its richness. QQ with long SRT would be a favorable operational strategy for effective MBR fouling control.
Tahir Iqbal, Syed Salman Ali Shah, Kibaek Lee, and Kwang-Ho Choo
Elsevier BV
Naresh Mameda, Hyeona Park, Syed Salman Ali Shah, Kibaek Lee, Chi-Wang Li, Vincenzo Naddeo, and Kwang-Ho Choo
Elsevier BV
Syed Salman Ali Shah and Kwang-Ho Choo
Elsevier BV
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
GRANT DETAILS
Engineered Membrane Development for Water Decontamination, 08/23–08/26. Special Talent Introduction and Scientific Research Project, Beijing Normal University (Principal Investigator)
Overseas Training Support Project, International Collaboration, BK21 Four, Ministry of Education, Science and Technology, South Korea, Year 2022 (Principal Investigator)
Biogasification and crystallization process-based sewage integrated resource recovery technology development, 09/20–08/23, NRF South Korea (Participant)
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
US Patent
Application. 18/565,354, “Membrane Filters for Water and Wastewater Treatment and Method of Producing the
Korean Patent
Patent No. 10-2554943-0000, “Membrane for Water Treatment and Methods of Producing the Same,” July 2023.
INDUSTRY EXPERIENCE
2018 – 2018: Executive Engineering at Qarshi Industries Pvt. Ltd., Pakistan
2017 – 2018: Process Engineer at Agritech (Formerly Pak-American Fertilizers LTD)