List of Publications
Journal publications, Peer-reviewed conference proceedings, and Book chapters
 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 (2019). (in press)
 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. (2019). (Accepted)
 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.
 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.
 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).
 Han, Xi, Xiaonan Wang*, and Kang Zhou*. "Develop machine learning-based regression predictive models for engineering protein solubility." Bioinformatics (2019).
 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.
 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.
 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).
 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.
 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 (2019).
 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.
 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.
 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)
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 Xiaonan Wang, Ahmet Palazoglu, and Nael H. El-Farra*. "Proactive optimization and control of heat-exchanger super networks." International Symposium on Advanced Control of Chemical Processes (ADCHEM), IFAC 48, 8 (2015): 592-597.
 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.
 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.
 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.
 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.
Check our latest papers
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
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.
Volume 228, 15 October 2018, Pages 1385-139
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.
supply chain design through
multi-objective stochastic programming
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.