Abstract

Bioelectricity is a promising alternative renewable energy source that can be produced from live plants and trees. However, previous experimental studies mostly applied non-sustainable bioelectricity extraction techniques from cut-off stem or leaves and neglected the optimum placement of electrodes for maximizing energy extraction without impeding plant growth. Electrode placement and penetration are crucial in energy extraction since they greatly influence electrical generated output enhancement. Relatively, along with the common plants used for bioelectricity extraction, the dragon fruit tree has the potential to be explored as an alternative bioelectricity source since it is widely abundant in many regions. With that, this work introduced a novel integrated genetic-population metaheuristic-based optimization model that was developed centered on in vivo stem bioelectricity extraction from dragon fruit tree to determine the exact optimum distance of silver-coated copper pin-type anodes and cathodes for maximum bioelectricity extraction through intercellular across vascular bundle (icVB) and inter-parenchymal cells (iPC) electrode penetration techniques, and incorporated the cradle-to-gate Life Cycle Assessment methodology to properly account the environmental impacts of the two intercellular penetration approaches. Multigene genetic programming was performed to formulate the fitness function followed by a comparative atom search (ASO), shuffle frog-leaping, and elephant herding-based bioelectricity harnessing optimization. Thus, ASO demonstrated the highest attainable fitness value and conformed well with both electrode placement treatments. This subsequently verified that ASO-based iPC penetration, yielding 58.923 J, surpasses icVB, which only yielded 13.909 J in terms of the total harnessed energy stored throughout the 30-day experiment. Overall, the genetic ASO-iPC with an electrode distance of 4.488 inches produced a higher yield of harnessed bioelectricity while incurring no significant damage and causing fewer environmental impacts compared to the ASO-icVB treatment. This developed technique can minimize greenhouse gas emissions while also expanding the application of evolutionary computing in agriculture and alternative energy domains.

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