Artificial neural network modeling and sensitivity analysis of performance and emissions in a compression ignition engine using biodiesel fuel
- Jaliliantabar, Farzad, Ghobadian, Barat, Najafi, Gholamhassan, Yusaf, Talal
- Authors: Jaliliantabar, Farzad , Ghobadian, Barat , Najafi, Gholamhassan , Yusaf, Talal
- Date: 2018
- Type: Text , Journal article
- Relation: Energies Vol. 11, no. 9 (2018), p. 1-24
- Full Text:
- Reviewed:
- Description: In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.
- Authors: Jaliliantabar, Farzad , Ghobadian, Barat , Najafi, Gholamhassan , Yusaf, Talal
- Date: 2018
- Type: Text , Journal article
- Relation: Energies Vol. 11, no. 9 (2018), p. 1-24
- Full Text:
- Reviewed:
- Description: In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.
An efficient network intrusion detection and classification system
- Ahmad, Iftikhar, Haq, Qazi, Imran, Muhammad, Alassafi, Madini, Alghamdi, Rayed
- Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Finding a place for organic waste-to-energy in Australian agribusiness
- Authors: Hurley, Craig
- Date: 2023
- Type: Text , Thesis , PhD
- Full Text:
- Description: This thesis seeks to understand Australian agribusiness engagement with approaches to generate energy from organic waste materials. Applications of modern bioenergy technologies, utilising agriculture residues to produce electrical, thermal and transport energy, have been well established in many parts of the world. There has been enthusiasm for bioenergy from agriculture to make a substantial contribution to Australia’s energy mix, but the agriculture sector, like Australia more generally, has been slow to transition to bioenergy technologies. Adopting the pragmatism research philosophy, this study applies the Multi-Level Perspective and Social Practice Approach frameworks to explore Australian agribusiness engagement with bioenergy systems, to produce energy from organic waste. A multi-methods qualitative research methodology is used to analyse the adoption of organic waste-to-energy approaches by Australian agribusiness, and to identify the critical drivers and barriers impacting these transitions. Except for sugar processors, Australian agribusiness adoption of organic waste-to-energy approaches is in its very early stages. The main drivers prompting agribusinesses to explore their organic waste-to-energy options are, agribusinesses experiencing problems with the cost and/or quality of their energy supplies, and/or problems with the social acceptance of their existing organic waste management practices. The main barriers to agribusinesses making the transition to bioenergy technologies, include financial factors such as the high capital costs of bioenergy plants and low returns on investment. Other barriers include a low level of awareness and understanding of bioenergy approaches in the agriculture industry, and in Australia more broadly, and a lack of consultative expertise to develop and service bioenergy systems. For organic waste-to-energy to play a more substantial role in Australian agriculture, support is needed to overcome critical barriers. This study finds policy and support mechanisms are required to encourage greater collaboration of small-scale agribusinesses and other relevant stakeholders. Investment is also needed to increase Australia’s awareness and understanding of organic waste-to-energy approaches, and to build the consultative expertise and skills-base to support the development of bioenergy systems.
- Description: Doctor of Philiosophy
- Authors: Hurley, Craig
- Date: 2023
- Type: Text , Thesis , PhD
- Full Text:
- Description: This thesis seeks to understand Australian agribusiness engagement with approaches to generate energy from organic waste materials. Applications of modern bioenergy technologies, utilising agriculture residues to produce electrical, thermal and transport energy, have been well established in many parts of the world. There has been enthusiasm for bioenergy from agriculture to make a substantial contribution to Australia’s energy mix, but the agriculture sector, like Australia more generally, has been slow to transition to bioenergy technologies. Adopting the pragmatism research philosophy, this study applies the Multi-Level Perspective and Social Practice Approach frameworks to explore Australian agribusiness engagement with bioenergy systems, to produce energy from organic waste. A multi-methods qualitative research methodology is used to analyse the adoption of organic waste-to-energy approaches by Australian agribusiness, and to identify the critical drivers and barriers impacting these transitions. Except for sugar processors, Australian agribusiness adoption of organic waste-to-energy approaches is in its very early stages. The main drivers prompting agribusinesses to explore their organic waste-to-energy options are, agribusinesses experiencing problems with the cost and/or quality of their energy supplies, and/or problems with the social acceptance of their existing organic waste management practices. The main barriers to agribusinesses making the transition to bioenergy technologies, include financial factors such as the high capital costs of bioenergy plants and low returns on investment. Other barriers include a low level of awareness and understanding of bioenergy approaches in the agriculture industry, and in Australia more broadly, and a lack of consultative expertise to develop and service bioenergy systems. For organic waste-to-energy to play a more substantial role in Australian agriculture, support is needed to overcome critical barriers. This study finds policy and support mechanisms are required to encourage greater collaboration of small-scale agribusinesses and other relevant stakeholders. Investment is also needed to increase Australia’s awareness and understanding of organic waste-to-energy approaches, and to build the consultative expertise and skills-base to support the development of bioenergy systems.
- Description: Doctor of Philiosophy
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