Erdal Aydin

Erdal Aydin
Koc University · Department of Chemical and Biological Engineering

PhD in Process Systems Engineering

About

31
Publications
4,220
Reads
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124
Citations
Citations since 2017
23 Research Items
119 Citations
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2017201820192020202120222023051015202530
2017201820192020202120222023051015202530
Introduction
My research interests lie within the areas of process systems engineering for creating efficient ways to harvest natural resources, reducing the costs of chemical production systems and minimizing the disposal of ecologically unfavorable materials. In process systems engineering, these objectives generally demand the integration of the broad topics such as process synthesis, identification, intensification, scheduling, monitoring, control and decision making under uncertainty. The integration of such topics brings about fairly complex, usually large-scale problems which must be formulated, analyzed and solved in a reliable and efficient manner.
Additional affiliations
August 2018 - July 2019
Massachusetts Institute of Technology
Position
  • PostDoc Position
August 2015 - July 2018
Max Planck Institute
Position
  • PhD Student
Education
September 2013 - August 2015
Koc University
Field of study
  • Chemical and Biological Engineering
September 2008 - June 2013
Middle East Technical University
Field of study
  • Chemical Engineering

Publications

Publications (31)
Article
Full-text available
Multi-energy microgrids comprise various energy sources such as solar, wind, hydro, biomass, oil, gas, and coal. Optimal configuration and scheduling of multi-energy microgrids enhance energy efficiency and reduce carbon dioxide emissions. In this work, a novel and detailed mixed-integer nonlinear programming (MINLP) model is proposed to compare th...
Article
Full-text available
Selection of input variables of the empirical models has vital effect on the prediction performance, reduced overfitting and reduced computational load. Various trials and error and sequential methods in the literature to deal with input selection for artificial neural networks (ANNs). However, these methods are not considered as automatic and syst...
Article
Full-text available
The performance of artificial neural networks (ANNs) is highly influenced by the selection of input variables and the architecture defined by hyper parameters such as the number of neurons in the hidden layer and connections between network variables. Although there are some black-box and trial and error based studies in the literature to deal with...
Article
Full-text available
Renewable energy integration into existing or new energy hubs together with Green technologies such as Power to Gas and Green Hydrogen has become essential because of the aim of keeping the average global temperature rise within 2 °C with regard to the Paris Agreement. Hence, all energy markets are expected to face substantial transitions worldwide...
Preprint
Full-text available
Investigation of energy systems integrated with green chemical conversion, and in particular combi-nation of green hydrogen and synthetic methanation, is still a scarce subject in the literature in terms of optimal design and operation for energy grids under weather intermittency and demand uncertain-ty. In this work, a multi-period mixed-integer l...
Article
Optimum selection of input variables, number of hidden neurons and connections among the network elements deliver the best configuration of an ANN, usually resulting in reduced over-fitting and improved test performance. This study focuses on the development of a superstructure-oriented feedforward ANN design and training algorithm whose impacts ar...
Chapter
Integration of process flowsheet simulators and optimization algorithms is a prominent approach to address simultaneous design and optimization of processes, which is represented by a mixed integer nonlinear programming (MINLP) formulation. In this study, DWSIM, a free and rarely used simulator, is used as a black-box function for the evaluation in...
Conference Paper
Full-text available
Artificial neural networks (ANNs) have been obtaining increasing attention over the past decades both due to enhanced computing power and data availability. Classical and feedforward ANNs propagate the input information to the succeeding layers through linear and nonlinear operations, incorporating the elements of input vector with different weight...
Article
Full-text available
Artificial Neural Networks (ANNs) may suffer from suboptimal training and test performance related issues not only because of the presence of high number of features with low statistical contributions but also due to their non-convex nature. This study develops piecewise-linear formulations for the efficient approximation of the non-convex activati...
Chapter
Optimum selection of input variables, number of hidden neurons and connections between the network elements delivers the best configuration of an artificial neural network (ANN), resulting in reduced over-fitting and improved performance. In this study, a superstructure-oriented ANN design and training algorithm is suggested and implemented on an i...
Chapter
Turkey is one of the richest countries in terms of renewable energy resources. At the same time, the largest portion of the account deficit of Turkey is due to energy import. Optimization studies for design, integration and management of renewable energy is therefore crucial in terms of increasing overall energy efficiency. In addition, energy sour...
Article
Full-text available
Artificial Neural Networks (ANNs) have been used in a wide range of applications for complex datasets with their flexible mathematical architecture. The flexibility is favored by the introduction of a higher number of connections and variables, in general. However, over-parameterization of the ANN equations and the existence of redundant input vari...
Article
Full-text available
Dynamic optimization plays an important role toward improving the operation of chemical systems, such as batch and semi-batch processes. The preferred strategy to solve constrained nonlinear dynamic optimization problems is to use a so-called direct approach. Nevertheless, based on the problem at hand and the solution algorithm used, direct approac...
Thesis
Full-text available
The trend towards high-quality, low-volume and high-added value production has put more emphasis on semi-batch processing due to its increased flexibility of operations. Dynamic optimization plays an important role toward improving the operation of batch and semi-batch. In addition, nonlinear model predictive control (NMPC) is also an important too...
Article
Full-text available
The trend towards high-quality, low-volume chemical production has put more emphasis on batch and semi-batch processing due to its increased operational flexibility. The transient behavior of these processes makes their real-time optimization very challenging. In particular, the large prediction horizons required in shrinking-horizon NMPC increase...
Conference Paper
Full-text available
Nonlinear model predictive control (NMPC) is an important tool to perform real-time optimization for batch and semi-batch processes. Direct methods are often the methods of choice to solve the corresponding optimal control problems, in particular for large-scale problems (Wächter and Biegler, 2006; Zavala and Biegler, 2009). However, the matrix fac...
Conference Paper
Full-text available
Dynamic optimization is an important task in the batch chemical industry. Given a reliable process model, dynamic optimization can be considered as a promising tool for reducing production costs, improving product quality and meeting safety and environmental restrictions. Dynamic optimization methods available in the literature belong to the catego...
Article
Full-text available
Nonlinear model predictive control (NMPC) is an important tool for the real-time optimization of batch and semi-batch processes. Direct methods are often the methods of choice to solve the corresponding optimal control problems, in particular for large-scale problems. However, the matrix factorizations associated with large prediction horizons can...
Article
Full-text available
This work considers the numerical optimization of constrained batch and semi-batch processes, for which direct as well as indirect methods exist. Direct methods are often the methods of choice, but they exhibit certain limitations related to the compromise between feasibility and computational burden. Indirect methods, such as Pontryagin’s Minimum...
Chapter
This paper proposes a PMP-based solution scheme with parsimonious parameterization of sensitivity-seeking arcs in order to reduce the computational complexity of constrained dynamic optimization problems. We tested our method on a binary batch distillation column and a two-phase semi-batch reactor for the hydroformylation of 1-dodecene. The perform...
Article
Diesel hydroprocessing is a refinery process by which the sulfur impurities are removed by hydrodesulfurization and the main product diesel is obtained by hydrocracking. The industrial Diesel Hydroprocessing Plant considered in this study consists of two hydrodesulfurization reactors and one hydrocracking reactor in series. The feed to the plant is...
Presentation
This work considers the numerical optimization of constrained batch and semi-batch processes, for which direct as well as indirect methods exist. Direct methods are often the methods of choice, but they exhibit certain limitations related to the compromise between feasibility and computational burden. Indirect methods, such as Pontryagin’s Minimum...
Presentation
Full-text available
This work considers the numerical optimization of constrained batch and semi-batch processes, for which direct as well as indirect methods exist. Direct methods are often the methods of choice, but they exhibit certain limitations related to the compromise between feasibility and computational burden. Indirect methods, such as Pontryagin’s Minimum...
Conference Paper
Full-text available
Diesel hydroprocessing is a refinery process by which the sulfur impurities are removed by hydrodesulfurization and the main product diesel is obtained by hydrocracking. The industrial Diesel Hydroprocessing Plant considered in this study consists of two hydrodesulfurization reactors and one hydrocracking reactor in series. The feed to the plant is...
Article
Full-text available
Diesel hydro-processing (DHP) is an important refinery process which removes the undesired sulfur from the oil feedstock followed by hydrocracking and fractionation to obtain diesel with desired properties. The DHP plant operates with varying feed-stocks. Also, changing market conditions have significant effects on the diesel product specifications...
Article
Diesel hydroprocessing is an important refinery process which consists of hydrodesulfurization to remove the undesired sulfur from the oil feedstock followed by hydrocracking and fractionation to obtain diesel with desired properties. Due to the new emission standards to improve the air quality, there is an increasing demand for the production of u...

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