Sustainable personnel scheduling supported by an artificial neural network model in a natural gas combined cycle power plant

Özder, Emir Hüseyin; Özcan, Evrencan; Eren, Tamer

doi:10.1002/er.5480

Cited by 14 publications

(8 citation statements)

References 37 publications

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“…By combining gas and thermal plants, the overall energy efficiency of the plant will be enhanced. Besides, the overall emissions from the residual gases are reduced [77].…”

Section: Combined Cycle Gas Power Plantsmentioning

confidence: 99%

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Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids

et al. 2020

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Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.

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“…By combining gas and thermal plants, the overall energy efficiency of the plant will be enhanced. Besides, the overall emissions from the residual gases are reduced [77].…”

Section: Combined Cycle Gas Power Plantsmentioning

confidence: 99%

“…The CHP-based power plants are fueled by various fuels, e.g., natural gas, fossil oil, and different fuel blends. With a CHP-based power plant, an approximate 30% GHG emission can be reduced compared to the conventional TPP [77].…”

Section: Combined Heat and Powermentioning

confidence: 99%

Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids

et al. 2020

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“…ANN has been effectively applied in modeling various chemical processes. [31][32][33] Hossain et al 28 employed ANN for modeling the production of hydrogen-rich syngas by dry reforming of methane. The multilayer perceptron (MLP) and radial basis function (RBF) neural networks were robust in predicting the hydrogen-rich syngas production by the dry reforming of methane.…”

Section: Introductionmentioning

confidence: 99%

“…ANN has been effectively applied in modeling various chemical processes 31‐33 . Hossain et al 28 employed ANN for modeling the production of hydrogen‐rich syngas by dry reforming of methane.…”

Section: Introductionmentioning

confidence: 99%

Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

2020

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Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R 2) > 0.9. A low mean square error of 3.024 × 10 −24 and 6.22 × 10 −15 for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.

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“…çalışmalarında; doğal gaz kombine çevrimli elektrik santralinde görevli personellerin çizelgelemelerini, görev bazlı olarak ANP ve hedef programlama yöntemi kullanarak yürütmüşlerdir. Yine Özder vd[22] doğal gaz kombine çevrimli elektrik santralinde personel çizelgeleme çalışmalarını yapay sinir ağı modeli ile destekleyerek yürütmüşlerdir. Çavdur vd [23].…”

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COVID-19 pandemi riski durumunda yetkinlik bazlı güvenlik personeli vardiya çizelgeleme probleminin çözümü

Cürebal

Eren

2021

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Assigning competence points to staff using multi-criteria decisionmaking  Considering the case of the staff being affected by the covid-19 pandemic virus  Using some of the criteria as a special constraint for certain departments Conclusion:Monthly shift assignments were successfully performed by evaluating the competencies of the personnel and their situation against the pandemic virus. The established mathematical model has achieved all three objectives consistently. Special restrictions for various departments can be added to the model thanks to 6 criteria related to the working area of the staff. In addition to a general arrangement in the model, special arrangement can be made easily for any department. In addition, an interface was designed to ease for authorized people to use.

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Sustainable personnel scheduling supported by an artificial neural network model in a natural gas combined cycle power plant

Cited by 14 publications

References 37 publications

Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids

Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids

Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

COVID-19 pandemi riski durumunda yetkinlik bazlı güvenlik personeli vardiya çizelgeleme probleminin çözümü

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