This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris a...This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris and Karanja oils is utilized,aiming to reduce free fatty acid content to 1%through single-step transesterification.Optimization reveals peak biodiesel yield conditions:1%catalyst quantity,91.47 min reaction time,56.86℃reaction temperature,and 8.46:1 methanol to oil molar ratio.The ANN model outperforms RSM in yield prediction accuracy.Environmental impact assessment yields an E-factor of 0.0251 at maximum yield,indicating responsible production with minimal waste.Economic analysis reveals significant cost savings:30%-50%reduction in raw material costs by using non-edible oils,10%-15%increase in production efficiency,20%reduction in catalyst costs,and 15%-20%savings in energy consumption.The optimized process reduces waste disposal costs by 10%-15%,enhancing overall economic viability.Overall,the widespread adoption of biodiesel offers economic,environmental,and social benefits to a diverse range of stakeholders,including farmers,producers,consumers,governments,environmental organizations,and the transportation industry.Collaboration among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution.展开更多
In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Senso...In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.展开更多
The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological...The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological factors is investigated.The analysis combines a traditional numerical approach with machine learning techniques.Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods.The artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error clustering.Parametric analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and micro-rotation.The Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)increase.Variations in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt numbers.Higher(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number distribution.Increased(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt numbers.These findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer properties.By combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.展开更多
基金the financial support provided for this research project entitled“Enhancement of Cold Flow Properties of Waste Cooking Biodiesel and Diesel”under the File Number A/RD/RP-2/345 for the above publication.
文摘This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris and Karanja oils is utilized,aiming to reduce free fatty acid content to 1%through single-step transesterification.Optimization reveals peak biodiesel yield conditions:1%catalyst quantity,91.47 min reaction time,56.86℃reaction temperature,and 8.46:1 methanol to oil molar ratio.The ANN model outperforms RSM in yield prediction accuracy.Environmental impact assessment yields an E-factor of 0.0251 at maximum yield,indicating responsible production with minimal waste.Economic analysis reveals significant cost savings:30%-50%reduction in raw material costs by using non-edible oils,10%-15%increase in production efficiency,20%reduction in catalyst costs,and 15%-20%savings in energy consumption.The optimized process reduces waste disposal costs by 10%-15%,enhancing overall economic viability.Overall,the widespread adoption of biodiesel offers economic,environmental,and social benefits to a diverse range of stakeholders,including farmers,producers,consumers,governments,environmental organizations,and the transportation industry.Collaboration among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution.
基金Research Supporting Project Number(RSP2024R421),King Saud University,Riyadh,Saudi Arabia.
文摘In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.
基金Deanship of Scientific Research at King Khalid University,Abha,Saudi Arabia,for funding this work through theResearch Group Project underGrant Number(RGP.2/610/45)funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R102)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological factors is investigated.The analysis combines a traditional numerical approach with machine learning techniques.Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods.The artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error clustering.Parametric analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and micro-rotation.The Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)increase.Variations in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt numbers.Higher(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number distribution.Increased(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt numbers.These findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer properties.By combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.
