Energy Systems Engineering / Enerji Sistemleri Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4752
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Article Citation - WoS: 2Citation - Scopus: 2Bayesian Uncertainty Quantification in Temperature Simulation of Borehole Heat Exchanger Fields for Geothermal Energy Supply(Pergamon-Elsevier Science Ltd, 2025) Mohammadi, Hesam Soltan; Ringel, Lisa Maria; Bott, Christoph; Erol, Selcuk; Bayer, PeterAccurate temperature prediction is crucial for optimizing the performance of borehole heat exchanger (BHE) fields. This study introduces an efficient Bayesian approach for improving the forecast of temperature changes in the ground caused by the operation of BHEs. The framework addresses the complexities of multi-layer subsurface structures and groundwater flow. By utilizing an affine invariant ensemble sampler, the framework estimates the distribution of key parameters, including heat extraction rate, thermal conductivity, and Darcy velocity. Validation of the proposed methodology is conducted through a synthetic case involving four active and one inactive BHE over five years, using monthly temperature changes around BHEs from a detailed numerical model as a reference. The moving finite line source model with anisotropy is employed as the forward model for efficient temperature approximations. Applying the proposed methodology at a monthly resolution for less than three years reduces uncertainty in long-term predictions by over 90%. Additionally, it enhances the applicability of the employed analytical forward model in real field conditions. Thus, this advancement offers a robust tool for stochastic prediction of thermal behavior and decision-making in BHE systems, particularly in scenarios with complex subsurface conditions and limited prior knowledge.Article Citation - WoS: 22Citation - Scopus: 24Design, Evaluation, and Optimization of an Integrated Proton Exchange Membrane and Double Flash Geothermal Based Organic Rankine Cycle Multi-Generation System Fed by a Biomass-Fueled Gasifier(Elsevier, 2024) Taheri, Muhammad Hadi; Seker, Utku; Akkurt, Gulden Gokcen; Mohammadpourfard, MousaThis study introduces an innovative approach by formulating and evaluating a synergistic biomass-geothermal structure, emphasizing optimized inter-component connections. The research stands out for its thorough analysis of parameter impacts on the system and variables, addressing an unexplored aspect in integrated energy systems. The multi-generation systems are the integration of a combined gasification gas turbine cycle, double flash geothermal cycle, and proton exchange membrane cycle for the generating power and hydrogen. The overall system and its subsystems are studied to explore how the performance of thermodynamics and the total cost rate are influenced by operating parameters. The best operational conditions for both subsystems and the overall system have been determined by analyzing the impact of operating parameters on the thermodynamic behavior and environmental impact through parametric studies. The findings indicate while Sabalan's current efficiency is 16.26 %, the system energy efficiency reached 24.89 % when coupled with other renewable source. To enhance the system's efficiency, a genetics algorithm was utilized to simultaneously optimize the total cost of exergy destruction and investment cost. The outcome of the multi-objective optimization revealed that the exergy efficiency of optimal point for the system is 29.8 % and a total investment cost is 6 (M$/year).Article Citation - WoS: 8Citation - Scopus: 11Design, Thermodynamic and Economic Evaluation, and Optimization of Gasoline Production From Refinery Furnaces Flue Gas(Elsevier, 2023) Nazerifard, Reza; Mohammadpourfard, Mousa; Heris, Saeed ZeinaliIn this paper, the conversion of refinery furnaces’ flue gas into gasoline through the MTG process is investigated. This approach not only reduces greenhouse gas emissions, but also produces a high-value product, providing economic incentives to adopt this technology. The proposed integrated system comprises an organic Rankine cycle, an amine-based carbon capture unit, a methanol synthesis unit, and an MTG unit. In this study, we evaluated the technical and economic aspects of this conversion process, including the thermodynamic and cost analysis, to assess its viability as a sustainable solution for mitigating CO2 emissions from refineries. Also, using response surface methodology combined with the Box-Behnken design, the proposed integrated system was optimized to minimize the gasoline production cost. The thermodynamic assessment concludes that the energy and exergy efficiencies of the overall system are 73.12% and 85.24%, respectively. The proposed system yields an annual gasoline production rate of >184 million liters. The estimated total capital investment for the proposed system is 172.16 M$, which the methanol synthesis unit with a share of 48.65% is the most expensive one. The results give a gasoline production cost of 1.58 $/kg or 4.28 $/gal for the optimized case. Also, hydrogen has the highest contribution in the production cost, so with a 20% decrease in the price of hydrogen, the production cost of gasoline decreases by 18.71%. With this rate of technological improvement, reductions in the price of hydrogen seem inevitable in not-so-distant years, which makes the proposed system of converting refinery furnaces’ flue gas into gasoline became desirable. © 2023 Elsevier LtdArticle Citation - WoS: 12Citation - Scopus: 13Integration of Psychological Parameters Into a Thermal Sensation Prediction Model for Intelligent Control of the Hvac Systems(Elsevier, 2023) Turhan, Cihan; Özbey, Mehmet Furkan; Lotfi, Bahram; Gökçen Akkurt, GüldenConventional thermal comfort models take physiological parameters into account on thermal comfort models. On the other hand, psychological behaviors are also proven as a vital parameter which affects the thermal sensation. In the literature, limited studies which combine both physiological and psychological parameters on the thermal sensation models are exist. To this aim, this study develops a novel Thermal Sensation Prediction Model (TSPM) in order to control the HVAC system by considering both parameters. A data-driven TSPM, which includes Fuzzy Logic (FL) model, is developed and coded using Phyton language by the authors. Two physiological parameters (Mean Radiant Temperature and External Temperature) and one psychological parameter (Emotional Intensity Score (EIS) including Vigour, Depression, Tension with total of 32 subscales) are selected as inputs of the model. Besides the physiological parameters which are decided intentionally considering a manual ventilated building property, the most influencing three sub- psychological parameters on thermal sensation are also selected in the study. While the physiological parameters are measured via environmental data loggers, the psychological parameters are collected simultaneously by the Profile of Mood States questionnaire. A total of 1159 students are participated to the questionnaire at a university study hall between 15th of August 2021 and 15th of September 2022. The results showed that the novel model predicted Thermal Sensation Vote (TSV) with an accuracy of 0.92 of R2. The output of this study may help to develop an integrated Heating Ventilating and Air Conditioning (HVAC) system with Artificial Intelligence – enabled Emulators that also includes psychological parameters. © 2023 Elsevier B.V.Article Citation - WoS: 3Citation - Scopus: 3Investigation of External Magnetic Field Effect on the Performance of Ferrofluid-Based Single-Phase Natural Circulation Loops(Elsevier, 2023) Bozkır, Selim Can; Çobanoğlu, Nur; Doğanay, Serkan; Karadeniz, Ziya Haktan; Elçioğlu, Elif Begüm; Turgut, AlpaslanThe goal of this study is to investigate the performance of a Single-Phase Natural Circulation mini Loop (SPNCmL) operating under the influence of an external magnetic field (EMF). For this purpose, a numerical SPNCmL model working with Fe3O4 ferrofluids (1-3 vol%.) under the influence of an EMF is developed to reflect the effect of a NdFeB permanent magnet with a remanence of 1.22 T located at the outlet of the cooler-end for the magnetic field generation. System characteristics such as temperature difference at heater-end (& UDelta;Theater) and maximum temperature (Tmax) and performance in terms of effectiveness (& epsilon;) are investigated. In addition, the effect of EMF on boundary layer energy transport along the cooler-end is evaluated in terms of the change in the local Nusselt number. Applying an EMF dramatically affects the system performance in terms of an increase in & UDelta;Theater and & epsilon;, respectively up to 34% and 25% compared to those with water. Tmax values are obtained by up to 9% higher for Fe3O4 ferrofluids compared to water, while applying EMF results in an increment in Tmax by up to 5%. Improved heat transfer performance by employing EMF at the cooler-end outlet of the SPNCmLs emphasizes their potential in cooling applications.Article Citation - WoS: 15Citation - Scopus: 16Proposal of Novel Exergy-Based Sustainability Indices and Case Study for a Biomass Gasification Combine Cycle Integrated With Liquid Metal Magnetohydrodynamics(Elsevier, 2023) Canpolat Tosun, Demet; Açıkkalp, Emin; Çağlar, Başar; Altuntaş, Önder; Hepbaşlı, ArifExergy is considered a way to sustainability. Exergy-based analyses have been recently widely used for performance assessment and comparison purposes of energy systems from production to end-user while different sustainability related indices or indicators including exergetic concepts have been developed in the literature. In this regard, the present study proposed five different indices: (i) Exergetic Fuel Based Environmental Remediation Index (X), (ii) Exergetic Product Based Environmental Remediation Index (delta), (iii) Exergetic Fuel Based Total Environmental Remediation Index (beta), (iv) Exergetic Product Based Total Environmental Remediation Index (alpha), and (v) Improved Sustainability Index (ISI). These indices were applied to a novel Biomass-integrated Gasification Combine Cycle (BIGCC) integrated with Liquid Metal Magnetohydrodynamics (LMMHD). They allowed to perform a more complete environmental analysis by considering the exergetic cost of environmental remediation of the process. The average exergy efficiency values for the BIGCC, LMMHD and the overall system were determined as 0.491, 0.222 and 0.688 under daily ambient temperatures for a year and different air to fuel ratio (AFR) conditions, respectively. The average values for.X, beta, delta, alpha and ISI were 1.636, 2.389, 1.949, 2.848 and 0.513, respectively.Article Citation - WoS: 49Citation - Scopus: 51Energy, Exergy, Exergoeconomic, and Exergoenvironmental (4e) Analysis of a New Bio-Waste Driven Multigeneration System for Power, Heating, Hydrogen, and Freshwater Production: Modeling and a Case Study in Izmir(Elsevier, 2023) Tabriz, Zahra Hajimohammadi; Mohammadpourfard, Mousa; Gökçen Akkurt, Gülden; Heris, Saeed ZeinaliToday, the world is facing numerous challenges such as the increasing demand for energy, fossil fuels reduction, the growth of atmospheric pollutants, and the water crisis. In the present research, a new multigeneration system based on urban sewage bio-waste has been designed and evaluated for power, hydrogen, freshwater, and heating production. This system, which consists of biomass conversion subsystem, hydrogen production unit, Brayton cycle, atmospheric water harvesting unit, steam Rankine cycle, and organic Rankine cycles, has been evaluated from a thermodynamic point of view, and the energy, exergy, exergoeconomic, and exergoenvironmental analyses have been carried out on it. In the current study, the atmospheric water harvesting unit, as an attractive and environmentally friendly technology, is integrated with this Biomass-based multigeneration. A case study has been conducted on this system using the information collected from cigli wastewater treatment plant located In Izmir province, Turkey, and the results indicate that such a system, in addition to receiving sewage sludge from the treatment plant unit as a polluting waste, can produce added value products. The modeling results show that in the base conditions and with a feed rate of 7.52 kg/s, the total power generated by this system is 17750 kW, the hydrogen production rate is 3180 kg/h, the freshwater production rate is more than 18 l/h, and the energy and exergy efficiencies are 35.48% and 40.18%, respectively. According to the exergoeconomic and exergoenvironmental evaluations, the unit cost of total products and the unit emission of carbon dioxide are calculated as 13.05 $/GJ and 0.2327 t/MWh, respectively. Also, the results of parametric studies show that increasing the rate of Biomass improves the overall energy efficiency and production rates and also reduces the unit emission of carbon dioxide, but on the other hand, it causes a decrease in exergy efficiency and an increase in the unit cost of total products.Article Citation - WoS: 7Citation - Scopus: 7Dynamic Development of Geochemical Reaction Fronts During Hydraulic Stimulation of Shale(Elsevier, 2023) Noel, Vincent; Druhan, Jennifer L.; Gündoğar, Aslı; Kovscek, Anthony R.; Brown Jr, Gordon E.; Bargar, John R.Injection of acidic hydraulic fracture fluid (HFF) into shale formations for unconventional oil/gas production results in chemical reactions in the shale matrix that can alter fluid transport. Here, we report the results of set of experiments designed to evaluate the impact of calcite dissolution as a function of carbonate mineral content on matrix chemical reactivity and pore-space modification concomitant with imbibition. We tracked acidic HFF transport in four samples of Wolfcamp shale with calcite contents varying from 4% to 59% by monitoring the rate and spatial extent of bromide tracer transport using synchrotron-based X-ray fluorescence microprobe (XFM) imaging. Concurrently, we also carried out XFM imaging of the spatial distribution of Ca in the Wolfcamp shale cores (as a proxy of calcite distribution). Our approach thus yields a direct record of time-resolved selective ion transport resulting from the penetration of acidic HFF and the associated mineral transformations in the shale cores. We show that the variability in calcite content of Wolfcamp shale samples can directly affect the rate and spatial extent of imbibition. Although reaction of the acidic HFF with carbonates in shales enhances calcite dissolution and increases porosity, the spatial extent of calcite dissolution in the shale matrix is limited due to a rapid neutralization of pH. The relative abundance and spatial distribution of calcite control the chemical saturation state of the HFF progressing into the matrix. As a result, calcite has a major impact on the spatial extent and rate of matrix alteration and thus on HFF transport during subsurface reservoir stimulation. Consequently, increased calcite content in the shale matrix inhibits the spatial extent of the pore-volume increase and, by extension, the spatial extent and rate of imbibition. Our results thus show that the overall rates of calcite dissolution approach the rates of acidic HFF transport (i. e., Damko spacing diaeresis hler number similar to 1), which could contribute to the efficiency of subsurface reservoir stimulation. A better understanding of HFF-calcite reaction rates is crucial for improving the prediction and optimization of fluid transport across HFF-shale interfaces during hydraulic fracturing.Article Citation - WoS: 45Citation - Scopus: 51Experimental Investigation of the Effect of Graphene/Water Nanofluid on the Heat Transfer of a Shell-And Heat Exchanger(Wiley-Hindawi, 2023) Zolfalizadeh, Mehrdad; Heris, Saeed Zeinali; Pourpasha, Hadi; Mohammadpourfard, Mousa; Meyer, Josua P. P.The most common type of heat exchanger used in a variety of industrial applications is the shell-and-tube heat exchanger (STHE). In this work, the impact of graphene nanoplate (GNP)/water nanofluids at 0.01 wt.%, 0.03 wt.%, and 0.06 wt.% on the thermal efficiency, thermal performance factor, pressure drop, overall heat transfer, convective heat transfer coefficient (CVHTC), and heat transfer characteristics of a shell-and-tube heat exchanger was examined. For these experiments, a new STHE was designed and built. The novelty of this study is the use of GNPs/water nanofluids in this new STHE for the first time and the fully experimental investigation of the attributes of nanofluids. GNP properties were analysed and confirmed using analyses including XRD and TEM. Zeta potential, DLS, and camera images were used to examine the stability of nanofluids at various periods. The zeta potential of the nanofluids was lower than -27.8 mV, confirming the good stability of GNP/water nanofluids. The results illustrated that the experimental data for distilled water had a reasonably good agreement with Sieder-Tate correlation. The maximum enhancement in the CVHTC of nanofluid with 0.06 wt.% of GNP, was equal to 910 (W/m(2)K), an increase of 22.47%. Also, the efficiency of the heat exchanger for nanofluid at 0.06 wt.% improved by 8.88% compared with that of the base fluid. The heat transfer rate of the nanofluid at maximum concentration and volume flow rate was 3915 (J/kg.K), an improvement of 15.65% over the base fluid. The pressure drops increased as the flow rate and concentration of the nanofluid increased. Although increasing the pressure drop in tubes would increase the CVHTC, it would also increase the power consumption of the pump. In conclusion, nanofluid at 0.06 wt.% had good performance.Article Citation - WoS: 52Citation - Scopus: 60Applied Machine Learning for Prediction of Waste Plastic Pyrolysis Towards Valuable Fuel and Chemicals Production(Elsevier, 2023) Cheng, Yi; Yang, Yang; Coward, Brad; Wang, Jiawei; Yıldız, Güray; Ekici, Ecrin; Yıldız, GürayPyrolysis is a suitable conversion technology to address the severe ecological and environmental hurdles caused by waste plastics' ineffective pre- and/or post-user management and massive landfilling. By using machine learning (ML) algorithms, the present study developed models for predicting the products of continuous and non-catalytically processes for the pyrolysis of waste plastics. Along with different input datasets, four algorithms, including decision tree (DT), artificial neuron network (ANN), support vector machine (SVM), and Gaussian process (GP), were compared to select input variables for the most accurate models. Among these algorithms, the DT model exhibited generalisable and satisfactory accuracy (R2 > 0.99) with training data. The dataset with the elemental composition of waste plastics achieved better accuracy than that with the plastic-type for predicting liquid yields. These observations allow the predictions by the data from ultimate analysis when inaccessible to the plastic-type data in unknown plastic wastes. Besides, the combination of ultimate analysis input and the DT model also achieved excellent accuracy in liquid and gas composition predictions. © 2023 The Authors