Erdal Aydin

Koc University · Department of Chemical and Biological Engineering

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...