A5060238679- Advanced Control Systems Optimization
- Microbial Metabolic Engineering and Bioproduction
- Algal biology and biofuel production
- Process Optimization and Integration
- Fault Detection and Control Systems
- Viral Infectious Diseases and Gene Expression in Insects
- Machine Learning in Materials Science
- Aquatic Ecosystems and Phytoplankton Dynamics
- Biofuel production and bioconversion
- Advanced Multi-Objective Optimization Algorithms
- Catalysis for Biomass Conversion
- Spectroscopy and Chemometric Analyses
- Innovative Microfluidic and Catalytic Techniques Innovation
- Model Reduction and Neural Networks
- Computational Drug Discovery Methods
- Biodiesel Production and Applications
- Biocrusts and Microbial Ecology
- Evolutionary Algorithms and Applications
- Mineral Processing and Grinding
- Scheduling and Optimization Algorithms
- Anaerobic Digestion and Biogas Production
- biodegradable polymer synthesis and properties
- Fuel Cells and Related Materials
- Enzyme Catalysis and Immobilization
- Neural Networks and Applications
University of Manchester
2018-2025
Imperial College London
2015-2024
Hebei University of Engineering
2024
Puyang Vocational and Technical College
2024
Delft University of Technology
2023
Institute of Electrical and Electronics Engineers
2016-2021
Machine Science
2020-2021
Engineering Systems (United States)
2020-2021
Amherst College
2020-2021
University of Massachusetts Amherst
1990-2021
Carboxyfluorescein-labeled brain tubulin has been microinjected into stamen hair cells of Tradescantia, and its distribution during mitosis cytokinesis was examined using confocal laser scanning fluorescence microscopy. The results show that incorporates plant microtubules is utilized throughout cytokinesis. Microtubule structures incorporate include the preprophase band, perinuclear sheath at late prophase, kinetochore fibers prometaphase, metaphase, anaphase, interzone spindle finally...
Nonlinear model predictive control (NMPC) is one of the few methods that can handle multivariable nonlinear controlsystems with constraints. Gaussian processes (GPs) present a powerful tool to identify required plant and quantifythe residual uncertainty plant-model mismatch. It crucial consider this uncertainty, since it may lead worsecontrol performance constraint violations. In paper we propose new method design GP-based NMPC algorithmfor finite horizon problems. The generates Monte Carlo...
Abstract Model‐based online optimization has not been widely applied to bioprocesses due the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack visualization techniques for ongoing processes. This study proposes an innovative hybrid framework which takes advantages both physics‐based data‐driven bioprocess monitoring, prediction, optimization. The initially generates high‐quality data by correcting raw process measurements via a noise filter (a...
Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae‐derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics kinetic modeling. However, approach presents intractability numerical instabilities when simulating large‐scale systems, causing time‐intensive computing efforts infeasibility in mathematical optimization....
Dynamic simulation is a valuable tool to assist the scale-up and transition of biofuel production from laboratory scale potential industrial implementation. In present study two dynamic models are constructed, based on Aiba equation, improved Lambert–Beer's law Arrhenius equation. The aims simulate effects incident light intensity, attenuation temperature upon photo-autotrophic growth hydrogen nitrogen-fixing cyanobacterium Cyanothece sp. ATCC 51142. results experimental data derived an...
Abstract Astaxanthin is a high‐value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides promising option to improve process sustainability. However, little effort has been made investigate the effects of multiple on X. biomass growth and astaxanthin production. Furthermore, construction high‐fidelity model challenging due system's variability, also known as batch‐to‐batch...
Abstract Predictive modeling of new biochemical systems with small data is a great challenge. To fill this gap, transfer learning, subdomain machine learning that serves to knowledge from generalized model more domain‐specific model, provides promising solution. While has been used in natural language processing, image analysis, and chemical engineering fault detection, its application within not systematically explored. In study, we demonstrated the benefits when applied predict dynamic...
Abstract Reinforcement learning (RL) is a data‐driven approach to synthesizing an optimal control policy. A barrier wide implementation of RL‐based controllers its data‐hungry nature during online training and inability extract useful information from human operator historical process operation data. Here, we present two‐step framework resolve this challenge. First, employ apprenticeship via inverse RL analyze data for synchronous identification reward function parameterization the This...