release of greenhouse gases linked to the manufacture of cement. Geopolymer as a binder for making concrete consists of two (2) main components; (1) the alkaline liquid consisting of sodium or potassium silicate and sodium or potassium hydroxide and (2) source material of geological origin or by-products rich in silica and alumina. The combination proportions utilised in this investigation were formulated utilising Scheffe's (5,2) basic lattice mix design approach with the intent to create the trial mix and the control mix. A total of thirty (30) geopolymers concrete sample mixes were made in the laboratory, with fifteen samples for trial mixes and fifteen mixtures for control mixes. These mixtures were used to appraise the performance of the sawdust ash geopolymer concrete in term of its flexural strength property. The study used sawdust ash as the source material and investigation revealed that subjecting sawdust ash to pyrolysis without oxygen has a notable impact on the pozzolanic characteristics of the constituent. Consequently, this also affects the flexural qualities of the concrete. Furthermore, it has been shown that softwood sawdust exhibits superior pozzolanic properties when compared to hardwood sawdust. The study revealed that the optimum flexural strength of sawdust ash blended geopolymer concrete is 3.3002 MPa and the corresponding mix deign obtained. Computer programs were created using Matlab and used for the optimization and prediction of the flexural strength of sawdust ash based geopolymer concrete.

This article proposes an innovative approach to optimizing scheduling in reconfigurable production systems, with a focus on minimizing resource allocation in a dynamic environment while considering time constraints and resource availability. We present a methodology based on intelligent Petri nets to model and solve this complex problem. Our approach aims to maximize operational efficiency and flexibility of production systems while ensuring optimal performance in the face of unforeseen events and changing market demands. We illustrate the effectiveness of our approach through a case study in a real industrial context, demonstrating the tangible benefits it offers in terms of optimizing production processes and reducing costs.

This study was prompted by the fact that nanofluid exhibits a completely different behavior from the base fluid, usually water. It was expected that there should be a reduction in the pumping power when pumping nanofluids as compared to pure liquids, without nanofluids. Pump action and their performance are defined in terms of their characteristic curves. These curves usually supplied by pump manufacturers are for water only. This research reveals performance curves for nanofluid and compares it with those obtained for water. By pumping separate concentrations of copper nanofluids which contains 5g, 10g, 15g and 20g of copper nanoparticles, through a constructed nanofluid pump testing machine, the various flow parameters obtained were used to characterize a one horse power centrifugal pump. The flow parameters included; time to pump 2.5, 5.0, and 7.5 liters of copper nanofluids, flow rate, head gained, Pump vibration due to pumping activities, pump speed, fluid power, brake power and the pump efficiencies. These parameters were used to plot performance characteristics curves for the different copper nanofluid concentrations which were then compared with those obtained for ordinary water. The results show a reduction in the pumping power as compared to pure liquids, without nanofluids. The performance characteristic curve obtained for water when compared to those obtained for copper nanofluids, revealed that there was an increased in pump efficiency at lower concentration of the copper nanofluid. Lastly, the relevance of the distinct properties of nanofluids to exhibit enhanced thermal conductivity and convective heat transfer coefficient compared to the base fluid was established.

This paper proposes a new high-gain Buck-Boost DC-DC converter, specifically designed for micro-grid applications where efficient voltage and power management is crucial. Traditional boost converters, such as those with switched inductors or capacitors, face limitations in voltage gain due to extreme operating cycles, leading to issues like reverse recovery, high conduction losses, and electromagnetic interference. Isolated converters, such as fly-back or push-pull converters, while effective at overcoming these constraints, introduce losses due to leakage inductance and overvoltage. With the rise of micro-grids and photovoltaic (PV) systems requiring high voltage gain due to their low output voltage, the proposed Buck-Boost DC-DC converter stands out for its ability to provide high output voltages while accommodating a wide range of input voltages. The converter is designed to handle input voltages ranging from 7V to 75V and uses pulse-width modulation (PWM)-based control to precisely regulate the output. Additionally, it incorporates advanced protection mechanisms with the LM5050-1, providing reverse input voltage protection and reduced quiescent current (IQ), ensuring enhanced safety and improved energy efficiency. Experimental results show that this Buck-Boost DC-DC converter significantly improves power management in microgrids, offering a reliable solution for renewable energy distribution systems and standalone networks. Its flexibility, robustness, and advanced protection features make it ideal for meeting the needs of next-generation power grids.

This study investigates the optimization and prediction of non-elastic performance factors required to augment the pipeline weldments' structural integrity and strength. The study's main focus is on the components of the operation, like the welding current, voltage, and gas flow rate to optimize and predict the corrosion rate of the pipeline weldment. Utilizing Design Expert software for experimental design and data analysis, the study employs the Central Composite Design (CCD) methodology to generate a quadratic model that predicts the responses effectively. The research also integrates Artificial Neural Networks (ANN) to further enhance the prediction accuracy. Experimental results indicate that the optimal welding parameters 160 amps current, 21.28 volts voltage, and 14.67 liters/min gas flow rate—yield a corrosion rate of 0.018 mm/yr. The study concludes that both RSM and ANN can be effectively used for optimization and prediction in welding processes, with RSM showing slightly better predictive capabilities.