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An Overview of Energy Technologies for a Sustainable Future
(Springer Int Publishing Ag, 2015) Esen, Ayse Nur; Duzgit, Zehra; Toy, A. Ozgur; Gunay, M. Erdem
Population and the economic growth are highly correlated with the energy demand. The world population was multiplied by a factor of 1.59 (reaching above 7 billion) from 1980 to 2013, while the total energy consumption of the world was multiplied by 1.84 (getting beyond 155,000 TWh) in the same time interval. Furthermore, the demand for energy is expected to increase even more with an average annual rate of 1.2 % in the near future. However, for the last 30 years, about 85-90 % of the energy demand is supplied by petroleum, natural gas, and coal, even though they are harmful for the environment and estimated to be depleted soon. Hence, building energy policies to satisfy the needs of increasing population and growing economy in a sustainable, reliable, and secure fashion has become quite important. This may involve optimizing the energy supplies, minimizing the environmental costs, promoting the utilization of clean and renewable energy resources and diversifying the type of energy sources. Thus, not only the conventional energy generation technologies must be developed more, but also environmentally friendly alternative energy sources (such as wind, solar, geothermal, hydro, and bio) must become more widespread to sustain the energy needs for the future. However, this requires a significant amount of research on energy technologies and an effective management of the energy sources.
Analysis and modeling of high-performance polymer electrolyte membrane electrolyzers by machine learning
(Pergamon-Elsevier Science Ltd, 2022) Gunay, M. Erdem; Tapan, N. Alper; Akkoc, Gizem
In this study, box and whisker and principal component analysis, as well as classification and regression tree modeling as a part of machine learning were performed on a database constructed on PEM (polymer electrolyte membrane) electrolysis with 789 data points from 30 recent publications. Box whisker plots discovered that pure Pt at the cathode surface, Ti at the anode support, the existence of Pt, Ir, Co, Ru at the anode surface, Ti porous structures at the electrodes, pure water-electrolyte and Nafion and Aquivion type membranes in proton exchange electrolyzer provide the highest performances. Principal component analysis indicated that when cathode surface consists of mostly pure Ni, when anode electrode has no support or vanadium (10-20%) doped TiO2 support and when anode electrode surface consists of cobalt-iron alloys (0.5:0.5 and 0.333:0.666 mol ratio) or RuO2, there is a risk for low-performance. Classification trees revealed that other than current density and potential, cathode surface Ni mole fraction, anode surface Co mole fraction are the most important variables for the performance of an electrolyzer. Finally, the regression tree technique successfully modeled the polarization behavior with a RMSE (root mean square error) value of 0.18. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Analysis of 70 years of change in benthic invertebrate biodiversity in the Prince's Islands region, Istanbul
(Elsevier, 2021) Huseyinoglu, Mehmet Fatih; Tari, Gokhan; Gunay, M. Erdem
Capital of several empires through centuries, Istanbul has been a populated city in history. However, the population has increased by a factor of 13.3 over the last seven decades, which in turn has made this megacity a hotspot of domestic and industrial pollution. Marine biodiversity around Istanbul suffered from the increased pressures due to overfishing, extensive recreational usage, extreme habitat destruction and introduction of alien species via heavy maritime traffic. 70 years before, Muzaffer Demir published a colossal marine biodiversity study in 1952. We used the organization of this publication, and particularly employed it as a methodology, combined with Underwater Visual Census for a later study between 1997-1999 which involved more than 100 scuba dives. Our study investigated the abundance status of benthic species living mainly on rocky reefs as deep as 50 meters. The investigated species represented four phyla: Cnidaria, Echinodermata, Mollusca and Porifera. Approximately 40% of cnidarians, 40% of echinoderms, 65% of molluscs, and 80% of sponges recorded in the 1950s, were not recorded during our study, particularly due to methodological differences and population collapses or local extinctions. This paper aims to analyze and compare the temporal aspects of marine biodiversity in the Prince's Islands and Bosphorus region using several time domains; historical records, Demir's compendium in 1952, our field study in the closing of the previous millennium, combined efforts of the Turkish Journal of Zoology in 2014 of Turkey biodiversity checklists, and the most recent literature after that. (C) 2021 Elsevier B.V. All rights reserved.
Analysis of lipid production from Yarrowia lipolytica for renewable fuel production by machine learning
(Elsevier Sci Ltd, 2022) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
In this work, biomass and lipid productivities of Yarrowia lipolytica were analyzed using machine learning techniques. A dataset containing 356 instances was constructed from the experimental results reported in 22 publications. The dataset was analyzed using decision trees to identify the features (descriptors) that lead to high biomass production, lipid content and lipid production. C/N ratio and fermentation time were found to be the most influential features for biomass production while the use of glucose and medium pH seemed to be more important for high lipid content. For the lipid production case, five generalizable paths leading to high values of this output were identified. One of those paths required pH to be < 6.3, high glucose and (NH4)(2)SO4 concentrations, lower concentration for yeast extract and the yeast strain not be H-222. Another one needed a pH greater than 6.3, a C/N ratio smaller than 75, a time greater than 14 h, and a strain other than W29. The same dataset was also explored deeper using association rule mining to determine the effects of individual features on output variables. It was then concluded that machine learning methods are very useful in determining the optimal conditions of biomass growth and lipid yield for Yarrowia lipolytica to produce renewable biofuels.
Analysis of past experimental data in literature to determine conditions for high performance in biodiesel production
(Wiley, 2016) Tapan, N. Alper; Yildirim, Ramazan; Gunay, M. Erdem
In this study, published experimental works on catalytic transesterification were analyzed to determine the most important variables affecting fatty acid conversion and the most suitable ranges of these variables for high performance. A database of 1324 data points was constructed from the experimental results in 31 representative papers published between 2008 and 2014, and this database was analyzed using artificial neural network (ANN) and decision tree (DT) techniques. It was found from ANN analysis that the most important variable for high fatty acid conversion was reaction time (with about 40% relative importance) followed by catalyst loading, alcohol:oil molar ratio, operating temperature, and support type with similar relative importance (about 10% each). DT analysis revealed 14 combinations of conditions leading to high performance, and some of these seemed to be generalizable for the use for the future studies; some heuristics were also derived from these generalizable conditions. (c) 2016 Society of Chemical Industry and John Wiley & Sons, Ltd
Analysis of the thermalization dynamics of two-layer thin films irradiated by femtosecond laser
(Elsevier Gmbh, 2020) Tunc, K. M. Murat; Gunay, M. Erdem; Bayata, Fatma
In this work, ultrafast thermalization dynamics was examined for a variety of two layer-thin films (Au/Si, Au/Ni, Au/W, Au/Al and Au/Pb). Non-equilibrium energy transport under laser irradiation was formulated for the electron and lattice sub-systems of the thin films. A significant reduction in the temperature of the electron and the lattice of the gold surface was observed especially for Au/Si and Au/Ni thin films due to their large G values. Next, the effects of laser power intensity and laser heating duration on the temperature distributions were examined for Au/Ni two-layer thin film. It was found that, as the laser intensity increased, the maximum electron temperature increased dramatically; on the other hand, as the pulse heating duration increased, the electron temperature gradually decreased. It was then concluded that thermal damage threshold of the gold surface can be improved by depositing gold layer on a substrate material with high electron-phonon coupling factor. Hence the thermal failure of thin films used in optical components of ultrafast laser systems or micro/nano electro mechanical systems can be prevented.
Application of Machine Learning for the Determination of Damaged Starch Ratio as an Alternative to Medcalf and Gilles Principle
(Springer, 2023) Tapan, N. Alper; Gunay, M. Erdem; Yildirim, Nilufer
As an alternative to the conventional amperometric method used for the determination of damaged starch ratio in wheat flour, two machine learning techniques were applied to a database constructed of 6264 voltammetric data obtained at two different electrodes, two different potassium iodide concentrations, and three different damaged starch ratios. Lift maps were extracted using association rule mining from the voltammetric database to describe electrode behavior and sensitivity to Chopin Dubois units (UCD) values. K-nearest neighbors (KNN) algorithm applied to the voltammetric experiments was able to predict UCD with higher accuracy when KI concentration was low. In addition, current quartiles, scatter, and shifts in lift maps and distinct regions after KNN classification showed that higher sensitivity towards damaged starch ratio is achieved on GC electrode and at low KI concentration.
CO2 capture over amine-functionalized MCM-41 and SBA-15: Exploratory analysis and decision tree classification of past data
(Elsevier Sci Ltd, 2019) Yildiz, Merve G.; Davran-Candan, Tugba; Gunay, M. Erdem; Yildirim, Ramazan
This study aims to extract knowledge for CO2 capture by amine-functionalized mesoporous silica (MCM-41 and SBA-15) through exploratory analysis and decision tree classification of the data reported in over 100 papers published between the years 2002 and 2017. A database containing 1039 data points showing the effects of 15 input variables (grouped in four as support properties, preparation method, amine properties and operational variables) over two performance variables as CO2 adsorption capacity and amine efficiency (CO2 captured/amino groups involved) was constructed. Box and whisker plots were applied (as a part of exploratory data analysis) to determine how various input variables influence the performance variables. Moreover, decision tree classification was used to determine the relative significance of the input variables and the possible combinations of these variables leading to high performance (to deduce heuristics for high CO2 uptake). It was found from the exploratory data analysis that amine density was the most significant variable affecting the adsorption capacity whereas remaining pore volume and adsorption temperature were the most influential variables in case of amine efficiency. Furthermore, various combinations of input variables leading to high CO2 capture performance were revealed through the decision tree analysis, all of which may be used as guidelines for future studies in this area.
Constructing global models from past publications to improve design and operating conditions for direct alcohol fuel cells
(Inst Chemical Engineers, 2016) Tapan, N. Alper; Gunay, M. Erdem; Yildirim, Ramazan
This work aims to analyze past publications on direct alcohol fuel cells (DAFC) in the literature using two data mining tools (artificial neural networks and decision trees) and to develop global models to predict the conditions leading to high performance of DAFC. The database constructed for this purpose contains 4682 data points over 271 polarization (IV) curves obtained from 36 publications in the literature. Decision tree classification models were used to develop heuristics to select the suitable fuel cell design and operational conditions to improve the maximum power density while artificial neural network models (ANN) were developed to test the predictability of IV curves at the conditions where experimental results were not available. The same ANN models were also used to determine the relative importance of design and operational variables to provide some insight to determine the variable to be manipulated. All these analyses were quite successful deducing some useful heuristics and models for the future studies from the continuously growing experience accumulated in the literature. (C) 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Decision tree analysis for efficient CO2 utilization in electrochemical systems
(Elsevier Sci Ltd, 2018) Gunay, M. Erdem; Turker, Lemi; Tapan, N. Alper
In this work, a database of 471 experimental data points excerpted from 34 different publications on electro-catalytic reduction of CO2 was formed. Firstly, the database was examined by exploratory data analysis using box and whiskers plots. Then, decision tree analysis was applied to determine the significance of the variables and to reveal the conditions leading to higher faradaic efficiency, production rate and product selectivity. It was found that Cu content smaller than 71% resulted high faradaic efficiencies depending on the amount of Sn, catholyte type, applied potential and pH of electrolyte. In this case, applied potential and Cu content were found to have the highest significance among all the input variables. On the other hand, the most generalizable combination of variables leading to high level of rate occurred when the Cu content being less than 13%, using a membrane other than Selemion AMV, employing a backing layer such as TGP-H-60 and keeping the applied potential between -1.5 and -2.6 V; for which the applied potential and CO2 flow rate were determined as the highest significant variables. Finally, the most generalizable path for the case of selectivity was obtained with Sn content higher than 15% and Cu content less than 52%, which leaded to formic acid production having the highest production rates. It was then concluded that, exploratory data analysis and decision trees can provide useful information to determine the conditions leading to higher CO2-electroreduction performance that may guide the future studies in this area.
Decision tree analysis of past publications on catalytic steam reforming to develop heuristics for high performance: A statistical review
(Pergamon-Elsevier Science Ltd, 2017) Baysal, Meltem; Gunay, M. Erdem; Yildirim, Ramazan
In this study, a database containing 5508 experimental data points was constructed for the steam reforming of methane using 81 papers (out of 453 initially screened) published between 2004 and 2014. The database was reviewed and analyzed with the help of decision trees to extract trends, heuristics and correlations, which are not visible to the naked eyes, through the vast experimental works accumulated in the literature over the years. The performance variable was selected as CH4 conversion while 21 variables related to catalyst preparation and operational conditions were used as input variables. It was found from a simple analysis of the literature that Ni, Rh, Ru and Pt are the most frequently used active metals, and they are generally applied over the supports of Al(2)0(3), CeO2 and ZrO2 usually using impregnation methods. A decision tree analysis was also applied to the database to determine the ranges of the catalyst preparation and operational conditions leading to high CH4 conversion. It was found for the Ni based catalysts that, even though the reaction temperature higher than 970 K is always required to achieve high CH4 conversion, some additional set of conditions are also needed; the combination of other variables especially support type and the feed composition seems to determine the catalytic performance. (C) 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Developing global reaction rate model for CO oxidation over Au catalysts from past data in literature using artificial neural networks
(Elsevier, 2013) Gunay, M. Erdem; Yildirim, Ramazan
In this work, the literature for CO oxidation kinetics over Au based catalysts was analyzed using artificial neural networks to test the possibility of developing global reaction rate models representing the entire literature. A database was constructed using the data obtained from nineteen papers published between the years 1997 and 2011; then, the reaction rate was modeled as a function of catalyst preparation and operational variables by using neural networks. Next, global reaction rate equations in the form of power law were developed for each support type by the help of the neural network model, and the order of reaction with respect to each reactant and the parameters of Arrhenius relation were estimated. These power law models were successfully validated by using the information reported in the literature; hence, it was concluded that they can be used for the initial estimation of the reaction rates in the absence of more specific rate equations. (C) 2013 Elsevier B.V. All rights reserved.
Evaluation of polymer electrolyte membrane electrolysis by explainable machine learning, optimum classification model, and active learning
(Springer, 2023) Gunay, M. Erdem; Tapan, N. Alper
In this work, a database of 789 experimental points extracted from 30 academic publications was used. The primary objective was to use novel machine-learning techniques to investigate how descriptor variables affect current density, power density, and polarization, and to identify rules or pathways that result in high current density, low power density, and low polarization. First, Shapley analysis was done to find and compare the magnitude of the contribution of each variable on current density as well as the positive and negative effects of all the variables. Then, correlation coefficient heat maps were provided to display the existence of any linear relationship between the input and output variables. Additionally, k-nearest neighbor classification (as an optimal model) was able to demonstrate the entire impact of all features on the outputs. Finally, the Bayesian optimization algorithm showed that the optimum performance of polymer electrolyte membrane electrolyzer could be reached with less experimental effort and time than the usual research plan. It was then concluded that machine learning methods can aid in determining the best conditions for designing a polymer electrolyte membrane electrolyzer to produce hydrogen, which can be used to guide the planning of future experiments. [GRAPHICS] .
Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning
(Pergamon-Elsevier Science Ltd, 2021) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
In this work, the algal biomass productivity and its lipid content were explored using a database containing 4670 instances extracted from the experimental results reported in 102 published articles. First, the influences of critical factors such as microalgae species, cultivation conditions, light intensity, CO2 amount, nutrient concentrations, reactor type, stress conditions, cell disruption methods, and lipid extraction solvents on the biomass and lipid production were reviewed. Then, the database was analyzed using machine learning techniques; decision trees were utilized to determine the combination of variables leading to high biomass and lipid content while association rule mining was used to find the specific conditions leading to very high biomass and lipid levels. Decision tree analysis discovered 11 different combinations of variables leading to high biomass productivity and 13 combinations for high lipid content; whereas, association rule mining analysis helped to identify the levels of specific factors for very high biomass and lipid production. It was then concluded that machine learning methods can help to determine the best conditions for optimum biomass growth and lipid yield for microalgae to manufacture renewable biofuels, and this can guide the planning of new experimental works. (C) 2020 Elsevier Ltd. All rights reserved.
Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey
(Elsevier Sci Ltd, 2016) Gunay, M. Erdem
In this work, the annual gross electricity demand of Turkey was modeled by multiple linear regression and artificial neural networks as a function population, gross domestic product per capita, inflation percentage, unemployment percentage, average summer temperature and average winter temperature. Among these, the unemployment percentage and the average winter temperature were found to be insignificant to determine the demand for the years between 1975 and 2013. Next, the future values of the statistically significant variables were predicted by time series ANN models, and these were simulated in a multilayer perceptron ANN model to forecast the future annual electricity demand. The results were validated with a very high accuracy for the years that the electricity demand was known (20072013), and they were also superior to the official predictions (done by Ministry of Energy and Natural Resources of Turkey). The model was then used to forecast the annual gross electricity demand for the future years, and it was found that, the demand will be doubled reaching about 460 TW h in the year 2028. Finally, it was concluded that the approach applied in this work can easily be implemented for other countries to make accurate predictions for the future. (C) 2015 Elsevier Ltd. All rights reserved.
Forecasting annual natural gas consumption using socio-economic indicators for making future policies
(Pergamon-Elsevier Science Ltd, 2019) Sen, Doruk; Gunay, M. Erdem; Tunc, K. M. Murat
Natural gas is a foreign-dependent source of energy in many countries and a rapid increase of its consumption is mainly associated with the increase of living standards and needs. In this work, Turkey was taken as a case study with high degree of foreign dependence of energy, and the future natural gas consumption was predicted by several different multiple regression models using socio-economic indicators as the descriptor variables. Among these, gross domestic product and inflation rate were found to be the only significant ones for this prediction. Next, three different projections for the future values of the significant descriptor variables were tested, and the natural gas consumption was predicted to rise gradually in the range 1.3 +/- 0.2 billion m(3) per year reaching to a consumption of 64.0 +/- 3.5 billion m(3) in the year 2025. It was then discussed that this additional natural gas can be compensated by utilizing local lignite sources or by starting a nuclear energy program although these two methods to reduce the future natural gas consumption have some conflictions with the general European energy matrix and environmental politics. Thus, it was concluded that resuming the wind and solar-based electricity generation programs can be considered as a more reasonable option. (C) 2019 Elsevier Ltd. All rights reserved.
Forecasting electricity consumption of OECD countries: A global machine learning modeling approach
(Elsevier Sci Ltd, 2021) Sen, Doruk; Tunc, K. M. Murat; Gunay, M. Erdem
Electricity is a critical utility for social growth. Accurate estimation of its consumption plays a vital role in economic development. A database that included past electricity consumption data from all OECD countries was prepared. Since national trends may be transferable from one country to another, the entire database was modeled and simulated via machine learning techniques to forecast the energy consumption of each country. Understanding similarities among the profiles of different countries could increase predictive accuracy and improve associated public policies.
Forecasting long-term world annual natural gas production by machine learning
(Elsevier Sci Ltd, 2023) Sen, Doruk; Hamurcuoglu, K. Irem; Ersoy, Melisa Z.; Tunc, K. M. Murat; Gunay, M. Erdem
The goal of this study is to model the global annual natural gas production using a variety of machine learning models in order to predict future production and determine a peak production date. World gross domestic product (GDP) based on purchasing power parities (at PPPs), inflation percentage, Henry Hub Price, Eum Price, cumulative natural gas resources, and annually discovered new resources were taken as descriptor variables, and Shapley analysis was conducted to observe the importance of features on the dataset. It was revealed according to this analysis that, Henry Hub price, inflation percentage, and newly discovered resources had minor effects on natural gas production, so they were left out. Then, a variety of machine learning algorithms were employed and the one with the highest prediction ability was found to be the stochastic gradient descent (SGD) algorithm. Next, this model was tested under four different scenarios, each with different GDP and natural gas price projections. Finally, natural gas production was found to reach its peak sometime between 2034 and 2046. It was then concluded that rather than relying on a traditional approach based on the Hubbert Curve, a machine learning model that takes into account all relevant factors can be used to accurately forecast natural gas production and its peak time, allowing governments and policymakers to make the necessary preparations.
Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012
(Pergamon-Elsevier Science Ltd, 2014) Odabasi, Cagla; Gunay, M. Erdem; Yildirim, Ramazan
In this work, a database (containing 4360 experimental data points) on water gas shift reaction (WGS) over Pt and Au based catalysts was constructed using the data obtained from the published papers between the years 2002 and 2012. Then, the database was analyzed using three data mining tools to extract knowledge in three areas: Decision trees to determine the empirical rules and conditions that lead to high catalytic performance (high CO conversion); artificial neural networks (ANNs) to determine the relative importance of various catalyst preparation and operational variables and their effects on CO conversion; support vector machines (SVMs) to predict the outcome of unstudied experimental conditions. It was concluded that, all three models were quite successful and they complement each other to extract knowledge from the past published works and to deduce useful trends, rules and correlations, which are not easily comprehensible by the naked eyes. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
Machine learning for algal biofuels: a critical review and perspective for the future
(Royal Soc Chemistry, 2023) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
In this work, machine learning (ML) applications in microalgal biofuel production are reviewed. First, the basic steps of algal biofuel production are summarized followed by a bibliometric analysis to demonstrate the major research trends in the field. Also, the major challenges related to the commercialization of technology are identified. Then, ML applications for various steps in the value chain are reviewed and analyzed systematically. Finally, a future perspective on the contribution of ML in the field is provided. Our analysis indicates that ML applications should focus on screening and selecting suitable strains, preferably together with some other value-added products, requiring close collaborations among the researchers in the field to construct an extensive microalgal strain database. Optimization of cultivation conditions appears to be another area where ML can be helpful. Although most published ML works on cultivation are not usually suitable to extract generalizable knowledge (due to the nonstandard nature of strains, wastewater, and irradiation), standard testing and methodologies related to reporting protocols should also be built through collaboration to build comparable and generalizable ML models.

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