Biocomputational Architecture Based on Particle Physics

Abstract

Domestic greywater produced via household chores has a major contribution to environmental pollution and is also the best-untapped energy source. Since the past decade, in the different fields, enormous efforts have been made to reach the bio-energy from bio-waste. These efforts consist of a wide range of thermochemical, bio-chemical, and microbial fuel cells; etc. however, all these efforts are in their infancy. They have high cost and are efficient on a large-scale; furthermore, these efforts used a non-intelligent process that has led to lack of development in this type of review. This ongoing research presents the smart design process that leads to hybrid energy via the intersection of computation, wastewater, and microorganisms and aims to introduce the novel bio-computational machine based on particle Physics. Therefore, we will investigate the mixture of the microbial fuel cells, specifically “Spirulina” micro-algae, and household chores greywater. In the computational framework, we propose high-precise fluid simulation with the advantage of particle position and dynamic physics for the conversion of the mixture of “Spirulina medium,” and household chore greywater makes energy control possible through flow, and management of the type of solution mix, by transferring data from the modules to the digital environment. Finally, these datasets simulate behavior based on the physical properties of each particle. The procedure works in a parametric computer-aided design (CAD) environment and through Grasshopper within Rhinoceros Software by syncing bio-computational machine to the digital environment. The solenoid valve and fluid flow controller applied between the nutritious tank and energy conversion tank measure nutritional consumption and sends it to the digital environment numerically with an Arduino-Uno board kit. These batch numerical signals are stored in our local database then are combined with the particle physics engine and simulate the material behavior; also, they provide the ability to control our bio-computational machine digitally.