Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of
            <i>Atriplex nitens</i>
            S.

Altikat, A.; Alma, Mehmet Hakkı

doi:10.1002/er.7441

Cited by 10 publications

(8 citation statements)

References 50 publications

(100 reference statements)

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“…Artificial neural networks (ANNs) use interconnected nodes to form a network that can process complex relationships. The relationship among nodes is as follows 42 :

a_{i} = σ (\underset{j}{false\sum} w_{italicij} o_{j}), σ (x) = \frac{1}{1 - e^{- x}}

where

a_{i}

denotes neuronal activity;

j

is a set of the neurons in the previous layer;

ω_{italicij}

represents the weight between two neurons;

o_{j}

is the output, and

σ (x)

is the activation function.…”

Section: Methodsmentioning

confidence: 99%

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Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Chou

Cheng

Liu

et al. 2022

Intl J of Energy Research

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Plant microbial fuel cells (PMFCs) are an emergent green-energy technology that continuously converts solar energy into electricity. Placing PMFCs on the roofs of urban buildings can help to create green urban environments even as they generate power. The power generation performance of PMFCs is affected by a range of environmental factors, so their power generation capacity is difficult to estimate. To develop an artificial intelligence model to forecast PMFC power generation accurately, relevant results obtained using shallow and deep learning techniques are compared for the first time. Once deep learning techniques had been identified as superior for this purpose, they were used with a bio-inspired optimization algorithm to dynamically setting the model hyperparameters. The developed model can also be applied to estimate the power generation capacity of PMFC devices in the future. The model was trained using data collected from sensors in a site experiment that was carried out using PMFCs embedded with Chinese pennisetumin (Pennisetum alopecuroides), narrowleaf cattail (Typha angustifolia), dwarf rotala (Rotala rotundifolia), and no plant as a control group. The original data of device parameters, environmental parameters, and the measured power generation of PMFCs in numerical form were applied to train shallow learning and time-series deep learning models. Meanwhile, the state-of-the-art sliding window technique was used to establish a numerical matrix, which was converted into a 2D image-like format to represent inputs for deep convolutional neural network (CNN) models. The accuracy in predicting the power generation capacity of PMFC devices showed that EfficientNet, an advanced type of CNN, was the best model among the shallow and deep learning techniques. These analytical results demonstrate the superior performance of deep CNNs in learning image features and their consequent suitability for constructing PMFC power generation forecasting models. To enhance the generalization performance of CNN, a

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“…Artificial neural networks (ANNs) use interconnected nodes to form a network that can process complex relationships. The relationship among nodes is as follows 42 :

a_{i} = σ (\underset{j}{false\sum} w_{italicij} o_{j}), σ (x) = \frac{1}{1 - e^{- x}}

where

a_{i}

denotes neuronal activity;

j

is a set of the neurons in the previous layer;

ω_{italicij}

represents the weight between two neurons;

o_{j}

is the output, and

σ (x)

is the activation function.…”

Section: Methodsmentioning

confidence: 99%

“…Artificial neural networks (ANNs) use interconnected nodes to form a network that can process complex relationships. The relationship among nodes is as follows 42 :…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Chou

Cheng

Liu

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

“…Therefore, they are able to work as black‐box models of multivariable static, and dynamic processes. Inspired by the functioning of the human brain, ANN structure is generally consisting of three parallel‐connected layers of neurons as input, hidden, and output 54‐56 . Each of these layers consists of a matrix of weight, a bias and output vector.…”

Section: Methodsmentioning

confidence: 99%

“…Inspired by the functioning of the human brain, ANN structure is generally consisting of three parallelconnected layers of neurons as input, hidden, and output. [54][55][56] Each of these layers consists of a matrix of weight, a bias and output vector. The input and output layer numbers are equal to the parameter number and response number, respectively.…”

Section: Methodology Of Artificial Neural Network (Ann) Modelingmentioning

confidence: 99%

A combined phenomenological artificial neural network approach for determination of pyrolysis and combustion kinetics of polyvinyl chloride

Özsin

Takan

et al. 2022

Intl J of Energy Research

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Summary As a widely used plastic material polyvinyl chloride (PVC) accounts for a significant amount of plastic waste but also offers great potential in conversion to chemical feedstock via pyrolysis process. However, development of a sensitive mathematical approach is required for proper process design and monitoring of thermochemical conversion processes. In this work, we attempt to develop an artificial neural network (ANN) model for estimation of mass loss as a function of temperature and heating rate during pyrolysis and combustion of PVC. For this purpose, pyrolysis and combustion characteristics were quantified using thermogravimetric analysis, then non‐isothermal kinetics were analysed by iso‐conversional models. The results of ANN models show that this method helps predict complex systems with high regression coefficient (R2) values. The best performed model analysed by ANN for pyrolysis was NN 7 with R2 = 0.9993, the best performed model for combustion was NN 10 with R2 = 0.9982. Comparison of experimental results to ANN predictions indicates that ANNs with a quick propagation algorithm can be an effective approach for modelling complex non‐linear systems such as thermal degradation of thermoplastics.

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“…Atriplex nitens su kıtlığı olan kurak, sıcak ve soğuk iklimler ve tuzlu topraklar gibi ekstrem toprak ve iklim şartlarına adaptasyon kabiliyeti yüksektir (Acar ve Güncan, 2002;Christman, 2003;Acar ve Dursun, 2012;Acar ve ark., 2019a). Atriplex nitens'in tuzlu alanlarda veriminin yüksek olduğu ve biogaz üretimi için ideal bir bitki olduğu belirlenmiştir (Akinshina ve ark., 2014a;Altikat and Alma, 2021). Yapılan bazı araştırmalarda Atriplex nitens bitkisinin yaş veriminin dekara 4000 kg'dan fazla olduğu, kuru madde ve ham protein içeriğinin yüksek olduğu belirlenmiştir (Carlsson ve Clarke, 1983;Acar ve Güncan, 2002;Acar, 2012;Akinshina ve ark., 2014b).…”

Section: Introductionunclassified

Kuru Şartlarda Yetiştirilen Selvi Sirken (Atriplex nitens)’in Ot Verimi ve Bazı verim Öğeleri Üzerine Farklı Ekim ve Hasat Dönemlerinin Etkileri

Keski̇n¹,

Temel²

2022

Türk Tarım Ve Doğa Bilimleri Dergisi

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Bu araştırma, Iğdır Üniversitesi Ziraat Fakültesi deneme alanında kıraç şartlarda yetiştirilen Selvi sirken (Atriplex nitens)'in ot verimi ve bazı verim özelliklerine ekim zamanları (Mart ortası, Mart sonu, Nisan başı ve Nisan ortası) ve hasat dönemlerinin (vejetatif dönem sonu, çiçeklenme başlangıcı ve tam çiçeklenme dönemi) etkilerinin belirlenmesi amacıyla yürütülmüştür. Deneme 2019 ve 2020 yıllarında tesadüf bloklarında bölünmüş parseller deneme planına göre 3 tekerrürlü olarak kurulmuştur. Araştırmada Selvi sirkenin bitki boyu, dal sayısı, ana sap kalınlığı, sap oranı, yaprak oranı, yaş ot verimi, kuru ot oranı ve kuru ot verimi belirlenmiştir. Araştırmada incelenen tüm özelliklere yıl, ekim zamanları ve hasat dönemlerinin etkileri önemli olmuştur. Araştırmanın birinci yılında ana sap kalınlığı, sap oranı ve kuru ot oranı, ikinci yılında ise bitki boyu, dal sayısı, yaprak oranı, yaş ot verimi ve kuru ot verimi daha yüksek bulunmuştur. Selvi sirkenin ilk ekim zamanı olan Mart ortasında ekilmesi bitki boyu, dal sayısı, ana sap kalınlığı, yaş ot verimi ve kuru ot veriminde artışlara neden olduğu belirlenmiştir. Selvi sirkende hasat döneminin geciktirilmesiyle bitki boyu ve sap oranı artarken, yaprak oranında ise azalma olduğu belirlenmiştir. Selvi sirken çiçeklenme döneminde hasat edilmesi durumunda ana sap kalınlığı, yaş ot verimi, kuru ot oranı ve kuru ot verimi daha yüksek olmuştur. Yıl, ekim zamanı ve biçim dönemlerine göre yaş ot verimi 3146.3 ile 9989.3 kg /da, kuru ot verimi ise 920.4 ile 3602.4 kg/da arasında değişmiştir. Sonuç olarak bölge kuru şartlarında Selvi sirken'in Mart ortasında ekimlerinin yapılması ve çiçeklenme başlangıcı döneminde hasat edilmesi durumunda daha yüksek yaş ve kuru ot alınacağı belirlenmiştir.

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Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

Cited by 10 publications

References 50 publications

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

A combined phenomenological artificial neural network approach for determination of pyrolysis and combustion kinetics of polyvinyl chloride

Kuru Şartlarda Yetiştirilen Selvi Sirken (Atriplex nitens)’in Ot Verimi ve Bazı verim Öğeleri Üzerine Farklı Ekim ve Hasat Dönemlerinin Etkileri

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