A5076305988- Advanced Control Systems Optimization
- Fault Detection and Control Systems
- Optimal Experimental Design Methods
- Innovative Microfluidic and Catalytic Techniques Innovation
- Advanced Multi-Objective Optimization Algorithms
- Viral Infectious Diseases and Gene Expression in Insects
- Platelet Disorders and Treatments
- Diabetes Management and Research
- Process Optimization and Integration
- Catalytic Processes in Materials Science
- Blood groups and transfusion
- Catalysis and Oxidation Reactions
- Control Systems and Identification
- Probabilistic and Robust Engineering Design
- Analytical Chemistry and Chromatography
- Pancreatic function and diabetes
- Diabetes and associated disorders
- Machine Learning in Materials Science
- Microfluidic and Capillary Electrophoresis Applications
- Catalysis and Hydrodesulfurization Studies
- Gold and Silver Nanoparticles Synthesis and Applications
- Antiplatelet Therapy and Cardiovascular Diseases
- Crystallization and Solubility Studies
- Catalysis for Biomass Conversion
- Complement system in diseases
University College London
2016-2025
University of Padua
2006-2021
Process Systems Enterprise (United Kingdom)
2021
Transnational Press London
2019
Nanomaterials Research (United States)
2018
Imperial College London
2007-2009
Bimetallic Au-Pd nanoparticles supported on TiO2 show excellent catalytic activity and selectivity to benzaldehyde in the solvent-free transformation of benzyl alcohol benzaldehyde, where toluene is main observed by-product, together with smaller amounts benzoic acid, benzoate dibenzyl ether. However, despite industrial relevance this reaction importance tuning desired only a few attempts have been made literature modeling kinetics for quantitative description system. A kinetic model...
The hydrodynamics of a three-phase micro-packed bed reactor and its effect on catalysed benzyl alcohol oxidation with pure oxygen were studied in silicon–glass microstructured reactor. microreactor was operated at 120 °C 1 barg contained channel 300 μm×600 μm cross-section, packed wt% Au–Pd/TiO2 catalyst, 65 average diameter. Improvements the conversion selectivity to benzaldehyde observed increasing gas-to-liquid ratio, which coincided change flow pattern from liquid-dominated slug...
Rapid estimation of kinetic parameters with high precision is facilitated by automation combined online Model-Based Design Experiments.
Advanced model-based experiment design techniques are essential for the rapid development, refinement, and statistical assessment of deterministic process models. One objective is to devise experiments yielding most informative data use in estimation model parameters. Current assume that multiple designed a sequential manner. However, equipment can sometimes be available, simultaneous (parallel) could advantageous terms time resources utilization. The concept parallel presented this paper....
The optimal model-based design of experiments aims at designing a set dynamic yielding the most informative process data to be used for estimation parameters first-principles model. According usual procedure parameter estimation, experiment is first designed offline; then, carried out in plant, and measurements are collected; finally, estimated after completion experiment. Therefore, information gathered during evolution analyzed only end itself. Since on basis estimates available before...
Rapid and precise estimation of kinetic parameters is facilitated by transient flow experiments designed using model-based design experiments.
The fourth industrial revolution is gaining momentum in the pharmaceutical industry. However, particulate processes and suspension handling remain big challenges for automation implementation of real-time particle size analysis. Moreover, development antisolvent crystallization often limited by associated time-intensive experimental screenings. This work demonstrates a fully automated modular platform that overcomes these bottlenecks. system combines crystallization, sample preparation,...
Industry 4.0 has birthed a new era for the chemical manufacturing sector, transforming reactor design and integrating digital twin into process control.
We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with QuantIfication UncERtainty for MultiphasE Systems), which is at intersection multi-physics and machine learning, aiming to enhance predictive capabilities in complex multiphase flow systems across diverse length time scales. Our contributions encompass variety approaches, including Design Experiments nanoparticle synthesis optimisation, Generalised Latent Assimilation models drop...
The global population increase leads to a high food demand, and reach this target products such as pesticides are needed protect the crops. Research is focusing on development of new that can be less harmful environment, mathematical models tools help understand mechanism uptake then guide in product phase. This paper applies systematic methodology model foliar pesticides, take into account uncertainties experimental data structure. A comparison between different conducted, identifiability...
The axial mixing/segregation behavior of single plastic particles in a bubbling fluidized bed reactor has been investigated by noninvasive X-ray imaging techniques the temperature range 500–650 °C and under pyrolysis conditions. Experimental results showed that extent mixing between particle increases as both fluidization velocity increase. Three modeling approaches were proposed to describe particle, i.e., purely mechanistic model, physics-informed neural network (PINN), an augmented PINN...
Abstract Gold nanoparticles (AuNPs) have gained prominence as versatile nanoscale building blocks in chemical and biomedical research. Liquid crystals (LCs) offer a promising composite matrix for fundamental research variety of applications. However, optimizing the solubility AuNPs within LC remains challenging due to interplay multiple experimental variables, necessitating extensive combinatorial trials. In this study, an automated AuNP synthesis platform combined with Design Experiment...
Continuous flow laboratory reactors are typically used for the development of kinetic models catalytic reactions. Sequential model-based design experiments (MBDoE) procedures have been proposed in literature where optimally designed discriminating amongst candidate or improving estimation parameters. However, effectiveness these is strongly affected by initial model uncertainty, leading to suboptimal solutions and higher number be executed. A joint (j-MBDoE) technique, based on...
Abstract Model‐based experiment design techniques are an effective tool for the rapid development and assessment of dynamic deterministic models, yielding most informative process data to be used estimation model parameters. A particular advantage model‐based approach is that it permits definition a set constraints on variables predicted responses. However, uncertainty in parameters can lead constrained procedure predict experiments turn out be, practice, suboptimal, thus decreasing...
An autonomous reactor platform was developed to rapidly identify a kinetic model for the esterification of benzoic acid with ethanol heterogeneous Amberlyst-15 catalyst. A five-step methodology studies employed systematically reduce number experiments required practical model. This included (i) initial screening using traditional factorial designed steady-state experiments, (ii) proposing and testing candidate models, (iii) performing an identifiability analysis reject models whose...
Type 1 diabetes mellitus is a disease affecting millions of people worldwide and causing the expenditure euros every year for health care. One most promising therapies derives from use an artificial pancreas, based on control system able to maintain normoglycaemia in subject affected by diabetes. A dynamic simulation model glucose−insulin can be useful several circumstances care, including testing glucose sensors, insulin infusion algorithms, decision support systems This paper considers...
Despite the high potential as feedstock for production of fuels and chemicals, industrial cultivation microalgae still exhibits many issues. Yield in systems is limited by solar energy that can be harvested. The availability reliable models representing key phenomena affecting algae growth may help designing optimizing effective at an level. In this work complex influence different light regimes on seawater alga Nannochloropsis salina represented first principles models. Experimental data...
An uncertainty-aware autonomous flow reactor platform was developed by combining automation and feedback optimization. The applied to identify appropriate kinetic models online for a gas–solid catalytic reaction.
Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One most challenging tasks kinetic model identification is precise estimation unknown parameters. This challenge can be effectively addressed through application Model-Based Design Experiments (MBDoE) techniques, which enable design experiments facilitating parameter with minimal runs analytical resources. Nevertheless, MBDoE techniques rely...