• List of Publications

    Journal publications, Peer-reviewed conference proceedings, and Book chapters


    [80] Li, Jiali, Mykola Telychko, Jun Yin, Yixin Zhu, ..., Jiong Lu and Xiaonan Wang*. "Machine Vision Automated Chiral Molecule Detection and Classification in Molecular Imaging." Journal of the American Chemical Society (2021).



    [79] Yuan, Xiangzhou, Manu Suvarna, Sean Low, Pavani Dulanja Dissanayake, Ki Bong Lee, Jie Li, Xiaonan Wang*, and Yong Sik Ok*. "Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons." Environmental Science & Technology (2021).



    [78] Suvarna, Manu, Ken Shaun Yap, Wentao Yang, Jun Li, Yen Ting Ng, and Xiaonan Wang*. "Cyber-physical production systems for data-driven, decentralized, and secure manufacturing—A perspective." Engineering (2021).



    [77] Jing, Rui, Xiaonan Wang*, Yingru Zhao*, Yue Zhou, Jianzhong Wu, and Jianyi Lin*. "Planning urban energy systems adapting to extreme weather." Advances in Applied Energy (2021): 100053.



    [76] Tian, Hailin, Xiaonan Wang*, Ee Yang Lim, Jonathan TE Lee, Alvin WL Ee, Jingxin Zhang, and Yen Wah Tong*. "Life cycle assessment of food waste to energy and resources: Centralized and decentralized anaerobic digestion with different downstream biogas utilization." Renewable and Sustainable Energy Reviews 150 (2021): 111489.



    [75] Li, Yinan, Song Lan, Morten Ryberg, Javier Pérez-Ramírez, and Xiaonan Wang*. "A quantitative roadmap for China towards carbon neutrality in 2060 using methanol and ammonia as energy carriers." iScience 24, no. 6 (2021): 102513.


    [74] Li, Jie, Lanjia Pan, Manu Suvarna, and Xiaonan Wang*. "Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening." Chemical Engineering Journal (2021): 131285.



    [73] Wang, Zhiyuan, Gade Pandu Rangaiah*, and Xiaonan Wang. "Preference Ranking on the Basis of Ideal-Average Distance Method for Multi-Criteria Decision-Making." Industrial & Engineering Chemistry Research (2021).



    [72] Liu, Haoyu, Chen Zhang, Hailin Tian, Lanyu Li, Xiaonan Wang*, and Tong Qiu*. "Environmental and techno-economic analyses of bio-jet fuel produced from jatropha and castor oilseeds in China." The International Journal of Life Cycle Assessment (2021): 1-14.



    [71] Shittu, Ekundayo*, Abhijai Tibrewala, Swetha Kalla, and Xiaonan Wang. "Meta-Analysis of the Strategies for Self-healing and Resilience in Power Systems." Advances in Applied Energy (2021): 100036.



    [70] Wang, Shukun, Lu Zhang, Chao Liu, Zuming Liu, Song Lan, Qibin Li*, and Xiaonan Wang*. "Techno-economic-environmental evaluation of a combined cooling heating and power system for gas turbine waste heat recovery." Energy (2021): 120956.



    [69] Liu, Zuming, Shukun Wang, Mei Qi Lim, Markus Kraft, and Xiaonan Wang*. "Game theory-based renewable multi-energy system design and subsidy strategy optimization." Advances in Applied Energy 2 (2021): 100024.



    [68] Li, Jie, Weijin Zhang, Tonggui Liu, Lihong Yang, Hailong Li, Haoyi Peng, Shaojian Jiang, Xiaonan Wang*, and Lijian Leng*. "Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification." Chemical Engineering Journal (2021): 130649.



    [67] Daniel Tan, Manu Suvarna, Yee Shee Tan, Jie Li, and Xiaonan Wang*. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing." Applied Energy (2021).



    [66] Mekki-Berrada, Flore, Ren, Zekun, Huang, Tan, ... and Xiaonan Wang*. "Two-step machine learning enables optimized nanoparticle synthesis." npj Computational Materials (2021).



    [65] Liu, Xiaoli, Yang Xu, Jiali Li, Xuanwei Ong, Salwa Ali Ibrahim, Tonio Buonassisi, and Xiaonan Wang*. "A robust low data solution: dimension prediction of semiconductor nanorods." Computers & Chemical Engineering (2021): 107315.



    [64] Li, Lanyu and Xiaonan Wang*. "Design and operation of hybrid renewable energy systems: current status and future perspectives." Current Opinion in Chemical Engineering, 31 (2021): 100669



    [63] Li, Jie, Xinzhe Zhu, Yinan Li, Yen Wah Tong, Yong Sik Ok, and Xiaonan Wang*. "Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource." Journal of Cleaner Production 278 (2021): 123928.



    [62] Xie, Qian, Manu Suvarna, Jiali Li, Xinzhe Zhu, Jiajia Cai*, and Xiaonan Wang*. "Online prediction of mechanical properties of hot rolled steel plate using machine learning." Materials & Design 197 (2021): 109201.



    [61] Jing, Lin, Qian Xie, Hongling Li, Kerui Li, Haitao Yang, Patricia Li Ping Ng, Shuo Li, Yang Li, Edwin Hang Tong Teo, Xiaonan Wang* and Po-Yen Chen*. "Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication." Matter 3, no. 6 (2020): 2160-2180.



    [60] Wang, Shukun, Chao Liu*, Jie Li, Zhuang Sun, Xiaoxue Chen, and Xiaonan Wang*. "Exergoeconomic analysis of a novel trigeneration system containing supercritical CO2 Brayton cycle, organic Rankine cycle and absorption refrigeration cycle for gas turbine waste heat recovery." Energy Conversion and Management 221 (2020): 113064.



    [59] Liu, Zuming, Mei Qi Lim, Markus Kraft, and Xiaonan Wang*. "Simultaneous design and operation optimization of renewable combined cooling heating and power systems." AIChE Journal (2020): e17039.



    [58] Li, Yinan, Song Lan, Javier Pérez-Ramírez*, and Xiaonan Wang*. "Achieving a low-carbon future through the energy–chemical nexus in China." Sustainable Energy & Fuels 4 (2020): 6141-6155



    [57] Wu, Nianyuan, Xiangyan Zhan, Xingyi Zhu, Zhihui Zhang, Jian Lin, Shan Xie, Chao Meng et al. "Analysis of biomass polygeneration integrated energy system based on a mixed-integer nonlinear programming optimization method." Journal of Cleaner Production 271 (2020): 122761.



    [56] Zhu, Xinzhe, Chi-Hung Ho, and Xiaonan Wang*. "Application of life cycle assessment and machine learning for high-throughput screening of green chemical substitutes." ACS Sustainable Chemistry & Engineering 8, no. 30 (2020): 11141-11151.



    [55] Zhou, Kang*, Wenfa Ng, Yoel Cortés-Peña, and Xiaonan Wang. "Increasing metabolic pathway flux by using machine learning models." Current Opinion in Biotechnology 66 (2020): 179-185.



    [54] Han, Xi, Wenbo Ning, Xiaoqiang Ma, Xiaonan Wang*, and Kang Zhou*. "Improving protein solubility and activity by introducing small peptide tags designed with machine learning models." Metabolic engineering communications 11 (2020): e00138.



    [53] Liu, Zuming, Yingru Zhao, and Xiaonan Wang*. "Long-term economic planning of combined cooling heating and power systems considering energy storage and demand response." Applied Energy 279 (2020): 115819.



    [52] Jiali Li, Kaizhuo Lim, Haitao Yang, Zekun Ren, Shreyaa Raghavan, Po-Yen Chen, Tonio Buonassisi*, and Xiaonan Wang*. "AI applications through the whole life cycle of material discovery." Matter 3 (2020): 1-40.



    [51] Li, Jie, Lanjia Pan, Manu Suvarna, Yen Wah Tong, and Xiaonan Wang*. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning." Applied Energy 269 (2020): 115166.



    [50] Manu Suvarna, Lennart Büth, Johannes Hejny, Mark Mennenga, Li Jie, Ng Yen Ting, Christoph Herrmann, and Xiaonan Wang*. "Smart manufacturing for smart cities – Overview, insights and future directions." Advanced Intelligent Systems (2020).



    [49] Wang, Wei, Yingru Zhao, Chuan Zhang and Xiaonan Wang*. "A load-complementarity combined flexible clustering approach for large-scale urban energy-water nexus optimization." Applied Energy 270 (2020): 115163.



    [48] Zhang, Jingxin, Hailin Tian, Xiaonan Wang* and Yen Wah Tong*. "Effects of activated carbon on mesophilic and thermophilic anaerobic digestion of food waste: process performance and life cycle assessment" Chemical Engineering Journal 399 (2020): 125757.



    [47] Wang, Chi-Hwa*, Yong Sik Ok, Siming You, and Xiaonan Wang. "The research and development of waste-to-hydrogen technologies and systems." Applied Energy 268 (2020): 115015.



    [46] Tian, Hailin, Xiaonan Wang, and Yen Wah Tong. "Sustainability assessment: focusing on different technologies recovering energy from waste." In Waste-to-Energy, pp. 235-264. Academic Press, 2020.



    [45] Yeo, Chester Su Hern, Qian Xie, Xiaonan Wang*, and Sui Zhang*. "Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning." Journal of Membrane Science (2020): 118135.



    [44] Zhang, Zhihui, Rui Jing, Jian Lin, Xiaonan Wang, Koen H. van Dam, Meng Wang, Chao Meng, Shan Xie, and Yingru Zhao*. "Combining agent-based residential demand modeling with design optimization for integrated energy systems planning and operation." Applied Energy 263 (2020): 114623.



    [43] Li, Lanyu, Xian Li, Clive Chong, Chi-Hwa Wang, and Xiaonan Wang*. "A decision support framework for the design and operation of sustainable urban farming systems." Journal of Cleaner Production (2020): 121928.



    [42] Li, Jie, Lanjia Pan, Manu Suvarna, Yen Wah Tong, and Xiaonan Wang*. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning." Applied Energy 269 (2020): 115166.



    [41] Lim, Kai Zhuo, Kang Hui Lim, Xian Bin Wee, Yinan Li, and Xiaonan Wang*. "Optimal allocation of energy storage and solar photovoltaic systems with residential demand scheduling." Applied Energy 269 (2020): 115116.



    [40] Oliver Inderwildi*, Chuan Zhang, Xiaonan Wang, and Markus Kraft. "The Impact of Intelligent Cyber-Physical Systems on the Decarbonization of Energy." Energy & Environmental Science 13, no. 3 (2020): 744-771.



    [39] Bowen Feng, Koen H. Dam, Miao Guo, Nilay Shah, Stephen Passmore, and Xiaonan Wang*. "Planning of food-energy-water-waste (FEW2) nexus for sustainable development." BMC Chemical Engineering 2.1 (2020): 1-19.



    [38] Zhuang, Rui, Xiaonan Wang*, Miao Guo, Yingru Zhao, Nael H. El-Farra, and Ahmet Palazoglu. “Waste-to-hydrogen: Recycling HCl to produce H2 and Cl2”. Applied Energy 259 (2020): 114184.



    [37] Saba, Tony, Joseph WH Burnett, Jianwei Li, Xiaonan Wang, James A. Anderson, Panagiotis N. Kechagiopoulos, and Xiaodong Wang*. "Assessing the environmental performance of NADH regeneration methods: A cleaner process using recyclable Pt/Fe3O4 and hydrogen." Catalysis Today 339 (2020): 281-8.





    [36] Tian Hailin, Jie Li, Miao Yan, Yen Wah Tong, Chi-Hwa Wang and Wang, Xiaonan*. "Organic waste to biohydrogen: A critical review from technological development and environmental impact analysis perspective." Applied Energy 256 (2019): 113961.


    [35] Han, Xi, Liheng Zhang, Kang Zhou* and Wang, Xiaonan*. "ProGAN: Protein Solubility Generative Adversarial Nets for Data Augmentation in DNN Framework." Computers & Chemical Engineering 124 (2019) (in press).



    [34] Zhu, Xinzhe, Wang, Xiaonan*, and Yong Sik Ok. "The application of machine learning methods for prediction of metal sorption onto biochars." Journal of Hazardous Materials 378 (2019), 120727.



    [33] Li, Jiali, Tiankai Chen, Kaizhuo Lim, Lingtong Chen, Saif A. Khan, Jianping Xie, and Wang, Xiaonan*. "Deep Learning Accelerated Gold Nanocluster Synthesis." Advanced Intelligent Systems. 1, no. 3 (2019): 1900029.



    [32] Li, Yinan, Wentao Yang, Ping He, Chang Chen, and Wang, Xiaonan*. "Design and management of a distributed hybrid energy system through smart contract and blockchain." Applied Energy 248 (2019): 390-405.



    [31] Zhu, Xinzhe, Yinan Li, and Wang, Xiaonan*. "Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions." Bioresource technology (2019): 121527.



    [30] Ascher, Simon, Ian Watson, Xiaonan Wang, and Siming You*. "Township-based bioenergy systems for distributed energy supply and efficient household waste re-utilisation: Techno-economic and environmental feasibility." Energy (2019).



    [29] Han, Xi, Xiaonan Wang*, and Kang Zhou*. "Develop machine learning-based regression predictive models for engineering protein solubility." Bioinformatics (2019).



    [28] Li, Lanyu, Zhiyi Yao, Siming You, Chi-Hwa Wang, Clive Chong, and Wang, Xiaonan*. "Optimal design of negative emission hybrid renewable energy systems with biochar production." Applied Energy 243 (2019): 233-249.



    [27] Wang, Xiaonan*, Koen H. van Dam, Charalampos Triantafyllidis, Rembrandt HEM Koppelaar, and Nilay Shah. "Energy-water nexus design and operation towards the sustainable development goals." Computers & Chemical Engineering 124 (2019): 162-171.



    [26] Yang, Huiying, Gökalp Gözaydın, Ricca Rahman Nasaruddin, Jie Ren Gerald Har, Xi Chen, Wang, Xiaonan*, and Ning Yan*. "Towards the Shell Biorefinery: Processing Crustacean Shell Waste Using Hot Water and Carbonic Acid." ACS Sustainable Chemistry & Engineering (2019).



    [25] Chen, Scarlett, Anikesh Kumar, Wee Chin Wong, Min-Sen Chiu, and Xiaonan Wang*. "Hydrogen value chain and fuel cells within hybrid renewable energy systems: Advanced operation and control strategies". Applied Energy 233 (2019): 321-337.



    [24] Li, Lanyu, Siming You, and Xiaonan Wang*. "Optimal Design of Standalone Hybrid Renewable Energy Systems with Biochar Production in Remote Rural Areas: A Case Study." Energy Procedia 158 (2019): 688-693.



    [23] Xiaonan Wang*, Wentao Yang, Sana Noor, Chang Chen, Miao Guo, and Koen H. van Dam. "Blockchain-based smart contract for energy demand management." Energy Procedia158 (2019): 2719-2724.



    [22] You, Siming, and Xiaonan Wang. "On the Carbon Abatement Potential and Economic Viability of Biochar Production Systems: Cost-Benefit and Life Cycle Assessment." In Biochar from Biomass and Waste, pp. 385-408. Elsevier, 2019. (Book Chapter)




    [21] Ho, Chi-Hung, Jieran Yi, and Xiaonan Wang*. "Biocatalytic Continuous Manufacturing of Diabetes Drug: Plantwide Process Modeling, Optimization, and Environmental and Economic Analysis." ACS Sustainable Chemistry & Engineering 7, no. 1 (2018): 1038-1051.



    [20] Jing, Rui, Meng Wang, Hao Liang, Xiaonan Wang, Ning Li, Nilay Shah, and Yingru Zhao*. "Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints." Applied Energy 231 (2018): 534-548.



    [19] Koppelaar, Rembrandt*, May Sule, Zoltán Kis, Foster Mensah, Xiaonan Wang, Charalampos Triantafyllidis, Koen Dam, and Nilay Shah. "Framework for WASH Sector Data Improvements in Data-Poor Environments, Applied to Accra, Ghana." Water 10, no. 9 (2018): 1278.



    [18] Sana Noor, Wentao Yang, Miao Guo, Koen H. van Dam, and Xiaonan Wang*. “Energy Demand Side Management within micro-grid networks enhanced by blockchain”, Applied Energy 228 (2018): 1385-1398.



    [17] Wong, Wee, Ewan Chee, Jiali Li, and Xiaonan Wang*. "Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing." Mathematics 6, no. 11 (2018): 242.



    [16] Xiaonan Wang*, Lanyu Li, Ahmet Palazoglu, Nael H. El-Farra, and Nilay Shah. "Optimization and control of offshore wind systems with energy storage." Energy Conversion and Management 173 (2018): 426-437.



    [15] Xiaonan Wang*, Miao Guo, Rembrandt H.E.M. Koppelaar, Koen H. van Dam, Charalampos Triantafyllidis, and Nilay Shah. "A nexus approach for sustainable urban Energy-Water-Waste systems planning and operation", Environmental science & technology 52, 5 (2018): 3257–3266.

    DOI: 10.1021/acs.est.7b04659


    [14] Niclas Bieber, Jen Ho Ker, Xiaonan Wang*, Koen H. van Dam, Charalampos Triantafyllidis, Rembrandt H.E.M. Koppelaar, and Nilay Shah. “Sustainable planning of the Energy-Water-Food nexus using decision making tools”, Energy Policy 113 (2018): 584-607.



    [13] Charalampos Triantafyllidis*, Rembrandt H.E.M. Koppelaar, Xiaonan Wang, Koen H. van Dam, and Nilay Shah. "An integrated optimisation platform for sustainable resource and infrastructure planning", Environmental Modelling & Software 101 (2018): 146-168.



    [12] Lanyu Li, Pei Liu, Zheng Li and Xiaonan Wang*. "A Multi-Objective Optimization Approach for Selection of Energy Storage Systems", Computers & Chemical Engineering 115 (2018): 213-225.



    [11] Noor, Sana, Miao Guo, Koen H. van Dam, Nilay Shah, and Xiaonan Wang*. "Energy Demand Side Management with supply constraints: Game theoretic Approach." Energy Procedia 145 (2018): 368-373.



    [10] Lanyu Li, and Xiaonan Wang*. "A Data-Driven Approach for Design and Optimization of Energy Storage Systems." In Computer Aided Chemical Engineering, vol. 44, pp. 1759-1764. Elsevier, 2018.



    [9] Scarlett Chen, Min-Sen Chiu, and Xiaonan Wang*. "Local control of fuel cell systems within hybrid renewable energy generation using model predictive control." Energy Procedia 145 (2018): 333-338.



    [8] Xiaonan Wang*, Qingyuan Kong, Maria M. Papathanasiou, and Nilay Shah. "Precision healthcare supply chain design through multi-objective stochastic programming." In Computer Aided Chemical Engineering, vol. 44, pp. 2137-2142. Elsevier, 2018.


    Before NUS


    [7] Xiaonan Wang, Ahmet Palazoglu, and Nael H. El-Farra*. "Optimal scheduling of responsive industrial production with hybrid renewable energy systems". Renewable Energy 100 (2017): 53–64.



    [6] Xiaonan Wang*, Koen H. van Dam, Charalampos Triantafyllidis, Rembrandt H.E.M. Koppelaar, and Nilay Shah. “Water and energy systems in sustainable city development: A case of Sub-Saharan Africa”. Procedia Engineering 198 (2017): 948-957.



    [5] Xiaonan Wang, Ahmet Palazoglu, and Nael H. El-Farra*. "Operational optimization and demand response of hybrid renewable energy systems." Applied Energy 143 (2015): 324-335.



    [4] Xiaonan Wang, Nael H. El-Farra, and Ahmet Palazoglu*. "Proactive reconfiguration of heat-exchanger super networks." Industrial & Engineering Chemistry Research 54, no. 37 (2015): 9178-9190.



    [3] Xiaonan Wang, Holger Teichgraeber, Ahmet Palazoglu, and Nael H. El-Farra*. "An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation." Journal of Process Control 24, 8 (2014): 1318-1327.



    [2] Xiaonan Wang, Chudong Tong, Ahmet Palazoglu, and Nael H. El-Farra*. "Energy management for the chlor-alkali process with hybrid renewable energy generation using receding horizon optimization." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC), pp. 4838-4843. IEEE, 2014.



    [1] Xiaonan Wang, Ahmet Palazoglu, and Nael H. El-Farra*. "Operation of residential hybrid renewable energy systems: Integrating forecasting, optimization and demand response." In American Control Conference (ACC), pp. 5043-5048. IEEE, 2014.


  • Special Issues Organized

    Published and On-going

    Waste-to-hydrogen New Development and Direction

    Wang, Chi-Hwa, Yong Sik Ok, Siming You, and Xiaonan Wang. "The research and development of waste-to-hydrogen technologies and systems." Applied Energy 268 (2020): 115015.

    Special Issue on Selected Papers from CIS-RAM2019—Cybernetics and Intelligent Systems (CIS) and Robotics, Automation and Mechatronics (RAM)

    Wang, Han, Wei Lin, and Xiaonan Wang. "Special Issue on Selected Papers from CIS-RAM2019—Cybernetics and Intelligent Systems (CIS) and Robotics, Automation and Mechatronics (RAM)." (2020): 1-2.

  • Publications Highlights

    Check our latest papers

    Renewable multi-energy systems have become a promising solution for deep decarbonization. However, government subsidy is needed to incentivize the deployment of renewable technologies for emission reduction. Here, we provide a game theory-based modeling framework along with tailored solution strategy for optimizing multi-energy system design and renewable subsidy strategies. We recommend an economic two-phase decarbonization pathway, namely first increasing renewable penetration to reduce dependence on fossil energy and then imposing a carbon emission cap to fulfill deep decarbonization. Our two-phase decarbonization pathway can be applied to other cities in China and worldwide, aiming to promote renewable energy penetration, reduce reliance on fossil fuels, and finally realize carbon neutral cities.

    A quantitative roadmap for China to achieve carbon neutrality by 2060 in methanol and ammonia based energy-chemical nexus

    iScience (2021)

    Carbon neutrality by 2060 is the recent expression of China’s international commitment to reduce its carbon dioxide emissions, which is an ambitious pledge for the world’s largest emitter of carbon dioxide. Energy and chemical sectors, the two main contributors for carbon dioxide emissions in China, are the biggest bottlenecks for reaching the objective of carbon neutrality. Moreover, coal-to-ammonia and coal-to-methanol are the major CO2 emission process contributors in China’s coal chemical sector. In order to push China toward carbon neutrality by 2060, departing from its conventional coal chemical production system and forming an integration between energy and chemical sectors would need to be implemented. Herein, a possible route to the carbon neutral target based on energy-chemical nexus for electricity generation as well as methanol and ammonia production is proposed in this study.

    Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions

    Bioresource technology (2019): 121527

    Machine learning was used to develop prediction models for yield and carbon contents of biochar (C-char) based on the pyrolysis data of lignocellulosic biomass, and explore inside information underlying the models. The results suggested that random forest could accurately predict biochar yield and C-char according to biomass characteristics and pyrolysis conditions. Furthermore, the relative contribution of pyrolysis conditions was higher than that of biomass characteristics for both yield (65%) and C-char (53%). The present work provided new insights for understanding pyrolysis process of biomass and improving biochar yield and C-char.

    Applied Energy 243 (2019): 233-249.

    To tackle the increasing global energy demand the climate change problem, the integration of renewable energy and negative emission technologies is a promising solution. In this work, a novel concept called “negative emission hybrid renewable energy system” is proposed for the first time. It is a hybrid solar-wind-biomass renewable energy system with biochar production, which could potentially provide energy generation, carbon sequestration, and waste treatment services within one system. A stochastic multi-objective decision-support framework is developed to maximize energy output and minimize greenhouse gas emissions by the optimal sizing of the energy components in the system.

    Computers & Chemical Engineering 115 (2018): 213-225.

    Get fascinated by the rapid development of energy storage technologies but have no clue what their true impacts are? Check out our recently published work on a decision-making framework for energy storage systems selection on our flagship PSE journal Computers & Chemical Engineering. Thanks for the great collaboration with Tsinghua Energy and Power Engineering and AquaBattery.

    Develop machine learning based predictive models for engineering protein solubility

    Bioinformatics (2019)

    Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. We first implemented a novel approach that predicted protein solubility in continuous numerical values instead of binary ones. After combing it with various machine learning algorithms, we achieved a prediction accuracy of 76.28% when Support Vector Machine (SVM) algorithm was used. Continuous values of solubility are more meaningful in protein engineering, as they enable researchers to choose proteins with higher predicted solubility for experimental validation, while binary values fail to distinguish proteins with the same value – there are only two possible values so many proteins have the same one.

    Applied Energy
    Volume 228, 15 October 2018, Pages 1385-139

    • Energy demand side management is optimized to improve the system performance.
    • A game theoretic approach for distributed energy management is developed.
    • Energy supply constraints and storage facilities are evaluated.
    • Blockchain technology is applied for efficient and trustable micro-grid operation.
    • Decentralized energy supply and demand management is implemented in practice.

    Environmental science & technology 52, 5 (2018): 3257–3266.

    Energy, water, and waste systems analyzed at a nexus level are important to move toward
    more sustainable cities. Waste-to-energy pathways, along with the water and energy sectors are studied, aiming to develop waste treatment capacity and energy recovery with the lowest economic and environmental cost. Three categories of waste including wastewater (WW), municipal solid waste (MSW), and agriculture waste are tested as the feedstock for thermochemical treatment via incineration, gasification, or pyrolysis for combined heat and power generation, or biological treatment such as anaerobic digestion (AD) and aerobic treatment.

    Energy Policy 113 (2018): 584-607.

    Open Access funded by Department for International Development

    Developing countries struggle to implement suitable electric power and water services, failing to match infrastructure with urban expansion. Integrated modelling of urban water and power systems would facilitate the investment and planning processes, but there is a crucial gap to be filled with regards to extending models to incorporate the food supply in developing contexts. In this paper, a holistic methodology and platform to support the resilient and sustainable planning at city region level for multiple sectors was developed for applications in urban energy systems (UES) and the energy-water-food nexus. Via a scenario based approach, innovative water supply and energy deployment policies are presented, which address the provision of clean energy for every citizen and demonstrate the potential effects of climate change.

    Renewable Energy 100 (2017): 53-64.

    This paper presents a methodology for the application of real-time optimization techniques to the problem of optimally scheduling and managing the interaction between electricity providers and users so that the grid and loads can come to an agreement to achieve optimal economic performance. The energy flows in typical industrial processes (e.g., chlor-alkali production) are simulated to illustrate day-ahead scheduling and contract following behaviors, as well as real-time demand response management.

    Applied Energy 143 (2015), 324–335

    This paper presents a methodology to systematically formulate a hybrid renewable energy system (HRES), which consists of solar, wind and diesel generator as a backup resource as well as battery storage, from the preliminary design stage to the optimal operation. Detailed modeling of each system component is introduced as the basis for the simulation study. System sizing considering energy flows is conducted to obtain the optimal combination of photovoltaic (PV) panels and wind turbines. Energy management strategies from both the demand-side and generation-side are developed to realize the objectives of meeting the electricity demand while minimizing the overall operating and environmental costs. Day-ahead and real-time weather forecasting, demand response and model updating are also integrated into the proposed methodology using a receding horizon optimization strategy.

    Precision healthcare
    supply chain design through
    multi-objective stochastic programming

    PSE (2018)

    Key issues in the cyclic supply chain for simultaneous design of the supply chain and the manufacturing plan of precision healthcare and medicines are discussed. A comprehensive optimization based methodology through both deterministic and stochastic programming is presented and applied to study the Chimeric Antigen Receptor (CAR) T cell therapies. Multiple objectives including maximization of the overall net present value (NPV) and minimization of the average response time of all patients are evaluated, while accounting the uncertainties in patients’ demand distribution.

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