Nikolay Cherkasov

Dr. Nikolay Cherkasov

orcid

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

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

Representative Publications

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1 April 2015 in Chemical Engineering and Processing - Process Intensification
Verified
Journal Article
Closed access
NCNikolay Cherkasov
Nikolay Cherkasov
AIA.O. Ibhadon
A.O. Ibhadon
PFP. Fitzpatrick
P. Fitzpatrick

1 October 2016 in Applied Catalysis B: Environment and Energy
Verified
Journal Article
Open access
BPB.S. Patil
B.S. Patil
NCNikolay Cherkasov
Nikolay Cherkasov
JLJ. Lang
J. Lang
AIA.O. Ibhadon
A.O. Ibhadon
VHVolker Hessel
Volker Hessel
QWQ. Wang
Q. Wang

26 October 2022 in Reaction Chemistry & Engineering
Verified
Journal Article
Open access
PDF file
NCNikolay Cherkasov
Nikolay Cherkasov
SASamuel J. Adams
Samuel J. Adams
EBEdward G. A. Bainbridge
Edward G. A. Bainbridge
JTJonty A. M. Thornton
Jonty A. M. Thornton
CSTRs and series: what are the typical applications, strengths and limitations in continuous process chemistry

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%.

22 September 2014 in The Journal of Physical Chemistry C
Verified
Journal Article
Closed access
DLDmitry B. Lukyanov
Dmitry B. Lukyanov
TVTanya Vazhnova
Tanya Vazhnova
NCNikolay Cherkasov
Nikolay Cherkasov
JCJohn L. Casci
John L. Casci
JBJohn J. Birtill
John J. Birtill

1 May 2015 in Applied Catalysis A: General
Verified
Journal Article
Closed access
NCNikolay Cherkasov
Nikolay Cherkasov
AIAlex O. Ibhadon
Alex O. Ibhadon
Alan McCueAlan McCue
Alan McCue
JAJames A. Anderson
James A. Anderson
SJShaun K. Johnston
Shaun K. Johnston