A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design

Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modelling. However, this approach presents computational intractability and numerical instabilities when simulating large-scale systems, causing time-intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data-driven surrogate modelling framework which considerably reduces computing time from months to days by exploiting state-of-the-art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi-objective optimization was incorporated to generate a Pareto-frontier for decision-making, advancing its applications in complex biosystems modelling and optimization

Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

In the last few years, bio-inspired optimization techniques have been widely adopted in fields such as computer science, mathematics, and biology in order to optimize solutions. Bio inspired optimization problems are usually nonlinear and restricted to multiple nonlinear constraints to tackle the problems of the traditional optimization algorithms, the recent trends tend to apply bio-inspired optimization algorithms which represent a promising approach for solving complex optimization problems. This work comprises state-of-art of ten recent bio-inspired algorithms, gap analysis, and its applications namely; Particle swarm optimization (PSO), Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithm (ESA), Cuckoo Search Optimization Algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm. The previous related works collected from Scopus databases are presented. Also, we explore some key issues in optimization and some applications for further research. We also analyze in-depth discussions on the essence of these algorithms and their connections to self-organization and their applications in different areas of research are presented. As a result, the proposed analysis of these algorithms leads to some key problems that have to be addressed in the future

Design of A Flexible, High-volume Direct Flue Gas-to-Algae Conversion Process for Value-added Bioproducts

Carbon conversion processes (where carbon dioxide is not only captured and stored, but converted to commercially-valuable products) have high barriers to sustained and large-scale commercial viability. Mass algae production as a means to carbon emissions reductions is considered to have great commercial potential, but the viability of deployment is going to rely on technological leaps supported by strong public and private investment. Presented in this Design Report is a novel pilot-scale microalgae cultivation system that successfully converts over 50% of the carbon dioxide (CO2) present in a stream of flue gas from a coal-red power plant flue gas into valuable biomass. The process operates at a carbon dioxide input rate of 200 kilograms per day from a 10% carbon dioxide by volume flue gas stream. A 9-15% Return on Investment over a 4-5 year period is reported. The project met a set of highly ambitious carbon conversion goals, and contributes to a positive outlook for the future of algae production for sustainable carbon emissions reduction and value-added product creation. This presented design should encourage investment into pilot-scale and demonstration facilities in a move towards large-scale operation. The presented process can be used to convert high volumes of CO2 to valuable biomass that can be sold for many applications ranging from feed to nutraceuticals to biofuels. The innovative Hybrid Petal Reactor design incorporates a central gas exchange column with a horizontal tubular photobioreactor system that harnesses sunlight during the day, and an internally- illuminated nighttime tank that involves innovative use of optics. The outdoor system was designed to scale with a series of tubes that branch out like petals from the central gas exchange column. The tube design is optimized for high productivity and low costs. An emphasis was placed on designing a process highly considerate of biological constraints. The presented final design offers great flexibility and opportunity for scale-up

A Review of Harmful Algal Bloom Prediction Models for Lakes and Reservoirs

Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxin’s effects on humans are not as well researched as the growth, behavior, and ecological niche of cyanobacteria, their health impacts are of large concern. It is important that research to mitigate and understand cyanobacterial blooms and cyanotoxin production continues. This project supports continued research by addressing an approach to collect and summarize published articles that focus on techniques and models to predict cyanobacterial blooms with the goal of understanding what research has been done to promote future work. The following report summarizes 34 articles from 2003 to 2020 that each describe a mechanistic or data driven model developed to predict the occurrence of cyanobacterial blooms or the presence of cyanotoxins in lakes or reservoirs with similar climates to Utah. These articles showed a shift from more mechanistic approaches to more data driven approaches with time. This resulted in a more individualistic approach to modeling, meaning that models are often produced for a single lake or reservoir and are not easily comparable to other models for different systems