ARTICLES:
27) Tarar, C., Aydin, E., Yetisen, A.K., Tasoglu, S. Bayesian Machine Learning Optimization of Microneedle Design for Biological Fluid Sampling, Sensors & Diagnostics, (in press)
26) Akulker, H., Aydin, E. Equipment Selection for Coupling a Microgrid with a Power-to-Gas System in the context of Optimal Design and Operation, Energy, (under review)
25) Tarar, C., Aydin, E., Yetisen, A.K., Tasoglu, S. Machine Learning-enabled optimization of interstitial fluid collection via sweeping microneedle design. ACS Omega, (in press)
24) Tatar, S., Aydin, E. Design and Operation of Renewable Energy Microgrids under uncertainty towards Green Deal and Minimum Carbon Emissions, Sustainable Energy, Grids and Networks, (under review)
23) Kaya, D., Koroglu, D., Aydin, E., Uralcan B. (2023) Optimization of capacitance in supercapacitors by constructing an experimentally validated hybrid artificial neural networks-genetic algorithm framework, Journal of Power Sources, Volume 568, 232987.
22) Koksal, E.S., Aydin, E. (2023) Physics Informed Piecewise Linear Neural Networks for Process Optimization. Computers & Chemical Engineering, Volume 174, 108244.
21) Asrav, T., Aydin, E. (2023) Physics-Informed Recurrent Neural Networks combined with Hyper-parameter Optimization for Dynamic Process Systems. Computers & Chemical Engineering, 173, 108195.
20) Akulker H., Sildir, H., Aydin, E. (2023). Optimum Design of a Microgrid and Establishment of a Long-Term Electricity Generation Plan with Mixed Integer Nonlinear Programming (MINLP). Afyon Kocatepe University Journal of Science and Engineering. 23 (1), 186-197
19) Akulker, H., Aydin, E. (2023). Optimal design and operation of a multi-energy microgrid using mixed-integer nonlinear programming: Impact of carbon cap and trade system and taxing on equipment selections, Applied Energy, 330, 120313.
18) Tatar, S. M., Akulker, H., Sildir, H., & Aydin, E. (2022). Optimal design and operation of integrated microgrids under intermittent renewable energy sources coupled with green hydrogen and demand scenarios. International Journal of Hydrogen Energy, 45(65), 27848.
17) Sildir, H. & Aydın, E. (2022). A systematic and efficient input selection method for artificial neural networks using mixed-integer nonlinear programming. Konya Mühendislik Bilimleri Dergisi , 10 (3) , 762-773 . DOI: 10.36306/konjes.1077177
16) Sildir, H., Sarrafi, S., & Aydin, E. (2022). Uncertainty propagation based MINLP approach for artificial neural network structure reduction. Processes, 10(9), 1716
15) Sildir, H., Sarrafi, S., & Aydin, E. (2022). Optimal artificial neural network architecture design for modeling an industrial ethylene oxide plant. Computers & Chemical Engineering, 107850.
14) Sildir, H., & Aydin, E. (2022). A mixed-integer linear programming based training and feature selection method for artificial neural networks using piece-wise linear approximations. Chemical Engineering Science, 249, 117273.
13) Erturk, E., Aydin, E., Sarrafi, S., Deliismail, O., Zahidova, A., H. Sildir “Superstructure Optimization of Dimethly Ether Process.” Computer Aided Chemical Engineering. Vol. 51. Elsevier, 2022. 661-665
12) Erturk, E. , Aydin, E. & Sildir, H. (2021). Reaction Network Reduction with Mixed-integer Nonlinear Programming. Konya Mühendislik Bilimleri Dergisi, DOI: 10.36306/konjes.970103
11) Sildir, H., Sarrafi, S., Aydin, E. “Data-driven Modeling of an Industrial Ethylene Oxide Plant: Superstructure-based Optimal Design for Artificial Neural Networks.” Computer Aided Chemical Engineering. Vol. 50. Elsevier, 2021. 445-450
10) Sildir, H., Akulker, H., Aydin, E. “A Probabilistic Scenario Generation Framework for Optimal Decision Making in Turkish Renewable Energy Market.” Computer Aided Chemical Engineering. Vol. 50. Elsevier, 2021. 1415-1420.
9) Sildir, H., Aydin, E., & Kavzoglu, T. (2020). Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization. Remote Sensing, 12(6), 956.
8) Aydin, E., Bonvin, D., Sundmacher, K. Toward Fast Dynamic Optimization: An Indirect Algorithm that Uses Parsimonious Input Parameterization. Industrial and Engineering Chemistry Research, 57 (2018): 38-48.
7) Aydin, E., Bonvin, D., Sundmacher, K. Computationally Efficient NMPC for Batch and Semi-Batch Processes using Parsimonious Input Parameterization. Journal of Process Control, 66 (2018): 12-22.
6) Aydin, E., Bonvin, D., Sundmacher, K. NMPC using Pontryagin’s Minimum Principle – Application to a two-phase Hydroformylation reactor under uncertainty. Computers & Chemical Engineering, 108 (2018): 47-56.
5) Aydin, E., Bonvin D., Sundmacher, K. Dynamic Optimization of Constrained Semi-batch Processes using Pontryagin’s Minimum Principle and Parsimonious Parameterization. In Computer Aided Chemical Engineering. 40 (2017). 2041-2046.
4) Aydin, E., Bonvin D., Sundmacher, K. Dynamic optimization of constrained semi-batch processes using Pontryagin’s Minimum Principle—An effective quasi-Newton approach. Computers & Chemical Engineering 99 (2017): 135-144.
3) Aydin, E., Arkun Y., Is, G. Economic Model Predictive of an Industrial Diesel Hydroprocessing Plant. IFAC-PapersOnLine, 49(7), 568-573.
2) Aydin, E., Arkun, Y., Is, G., Mutlu, M., & Dikbas, M. (2016). Plant-wide optimization and control of an industrial diesel hydro-processing plant. Computers & Chemical Engineering, 87,
1) Aydin, E., Celebi, A. D., Sildir, H., Arkun, Y., Canan, U., Is, G., & Erdogan, M. (2015). Dynamic modeling of an industrial diesel hydroprocessing plant by the method of continuous lumping. Computers & Chemical Engineering, 82, 44-54.
CONFERENCES:
10) World Hydrogen Energy Conference – WHEC – 2022, Istanbul
An MINLP based Optimal Design and Scheduling of a PTG System: A Case Study from Turkey
H.Akulker, E.Aydin
9) ESCAPE 2022, Toulose, France
Superstructure Optimization of a Dimethyl Ether Process
E.Erturk, E.Aydin, H. Sildir
8) AICHE Annual Meeting 2021, Boston (Virtual)
Optimal Artificial Neural Network Architecture Synthesis and Input Selection
H.Sildir, E.Aydin
7) ESCAPE 2021, Istanbul,Turkey
A Probabilistic Scenario Generation Framework for Optimal Decision Making in Turkish Renewable Energy Market
H.Sildir, H.Akulker, E.Aydin
6) ESCAPE 2021, Istanbul,Turkey
Data-driven Modeling of an Industrial Ethylene Oxide Plant: Superstructure-based Optimal Design for Artificial Neural Networks
H.Sildir, S.Sarrafi, E.Aydin
5) American Institute of Chemical Engineering ’17 Annual Meeting, Minneapolis, USA
NMPC of Semi-Batch Processes under uncertainty using Pontryagin’s Minimum Principle
E.Aydin, D.Bonvin, K.Sundmacher
4) World Congress of Chemical Engineering – Escape 2017, Barcelona, Spain
Dynamic Optimization using PMP and Parsimonious Parameterization
E.Aydin, D.Bonvin, K.Sundmacher
3) American Institute of Chemical Engineering ’16 Annual Meeting, S. Fransisco, USA
Dynamic Optimization of Constrained Semi-Batch Processes using Pontryagin’s Minimum Principle
E.Aydin, D.Bonvin, K.Sundmacher
2) 14th IFAC Symposium on DYCOPS-Norway, Trondheim, 6-8 June 2016
Economic Model Predictive of an Industrial Diesel Hydroprocessing Plant
E. Aydin, Y.Arkun, G.Is
1) American Institute of Chemical Engineering ’14 Annual Meeting, Atlanta, USA
Modeling of an Industrial Diesel Hydro-processing Plant by the Continuous Lumping Approach
D. Celebi, E. Aydin, H. Sildir, Y. Arkun, U. Canan, O. Kartal, M. Erdogan