Antonio Jose Exposito

Dr. Antonio Jose Exposito

Department of Chemical Engineering, University of Bath, Bath BA2 7AY, UK

Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2015 in Applied Catalysis B: Environment and Energy
Verified
Journal Article
Closed access
JMJ.M. Monteagudo
J.M. Monteagudo
ADA. Durán
A. Durán
RGR. González
R. González
Antonio Jose ExpositoAntonio Jose Exposito
Antonio Jose Exposito

9 August 2018 in Reaction Chemistry & Engineering
Verified
Journal Article
Closed access
PDF file
NCNikolay Cherkasov
Nikolay Cherkasov
YBYang Bai
Yang Bai
Antonio Jose ExpositoAntonio Jose Exposito
Antonio Jose Exposito
Evgeny RebrovEvgeny Rebrov
Evgeny Rebrov
Flow chemistry is a time-saver in the laboratory and a cost-saver in industry partly because of automation and autonomous operation. Nevertheless, a batch process is often preferred over a flow counterpart because setting up the autonomous operation may take a lot of time. In this paper, we propose a novel open-access OpenFlowChem platform based on LabVIEW for process automation, control and monitoring. The platform is optimized for quick system setup, reconfiguration and high flexibility. The platform is demonstrated in three examples: autonomous operation with an automatic stepwise program, proportional–integral–derivative (PID) control and self-optimization. In the first example, the system automatically executed a reaction program defined in a spreadsheet file to study the reversibility of a Pd/SiO2 catalyst poisoning with quinoline in the reaction of alkyne semihydrogenation. The addition of quinoline increased alkene selectivity and reduced the catalyst activity, but the time required to remove the catalyst poison varied by a factor of 10 and depended on the poison concentration. In the second example, a PID controller adjusted the nitrobenzene concentration in a hydrogenation reaction to compensate for catalyst deactivation and a disturbance in process parameters. The PID controller kept constant the hydrogen consumption determined by an inline optical liquid sensor. In the third example, the product yield in alkyne semihydrogenation was self-optimized, adjusting the flow rates of the substrate, the catalyst poison (quinoline) and the solvent in a tube reactor coated with a 5 wt% Pd/SiO2 catalyst. As a result, the alkene yield reached 96.5%.

1 January 2017 in Separation and Purification Technology
Verified
Journal Article
Closed access
KDKonstantina Davididou
Konstantina Davididou
JMJosé María Monteagudo
José María Monteagudo
ECEfthalia Chatzisymeon
Efthalia Chatzisymeon
ADAntonio Durán
Antonio Durán
Antonio Jose ExpositoAntonio Jose Exposito
Antonio Jose Exposito

1 April 2016 in Journal of Hazardous Materials
Verified
Journal Article
Closed access
JMJ.M. Monteagudo
J.M. Monteagudo
ADA. Durán
A. Durán
JLJ. Latorre
J. Latorre
Antonio Jose ExpositoAntonio Jose Exposito
Antonio Jose Exposito

1 January 2018 in Journal of Hazardous Materials
Verified
Journal Article
Closed access
Antonio Jose ExpositoAntonio Jose Exposito
Antonio Jose Exposito
JMJ.M. Monteagudo
J.M. Monteagudo
ADA. Durán
A. Durán
IMI. San Martín
I. San Martín
LGL. González
L. González

1 December 2018 in Journal of Cleaner Production
Verified
Journal Article
Open access
KDK. Davididou
K. Davididou
RNR. Nelson
R. Nelson
JMJ.M. Monteagudo
J.M. Monteagudo
ADA. Durán
A. Durán
Antonio Jose ExpositoAntonio Jose Exposito
Antonio Jose Exposito
ECE. Chatzisymeon
E. Chatzisymeon