Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Abstract

The growing potential of sustainable materials such as polyhydroxyalkanoates (PHAs), polylactic acid (PLA), alginate, carrageenan, and ulvan for bioplastics production presents an opportunity to promote a sustainable circular economy. This review investigates their properties, applications, and challenges. Bioplastics derived from algae offer an environmentally friendly alternative to petroleum‐based plastics, a shift of paramount importance to society due to the escalating environmental concerns associated with traditional plastics. The role of the internet‐of‐things (IoT) and machine learning in refining these bioplastics' production and development processes is emphasized. IoT monitors cultivation conditions, data collection, and process control for more sustainable production. Machine learning can enhance algae cultivation, increasing the supply of raw materials for algal bioplastics and improving their efficiency and output. The study results indicate the promise of algae‐based bioplastics, IoT, and machine learning in fostering a more environmentally sustainable future. By harnessing these advanced technologies, optimization of bioplastic production is possible, potentially revolutionizing the materials industry and addressing existing challenges toward achieving a sustainable circular economy.

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

“…Another critical research direction is the application of ML in biopolymer recycling and waste reduction. ,, As the world grapples with plastic pollution, biopolymers offer a sustainable alternative. ML can be leveraged to improve recycling processes, optimize waste management systems, and develop new biodegradable materials.…”

“…The intersection of ML and biobased polymers represents a significant and rapidly evolving field for research. This convergence unlocks unprecedented opportunities for developing sustainable energy, fuels, and biochemical solutions. , As we delve into this dynamic domain, the potential for innovative breakthroughs that can drive us toward a more sustainable future is immense.…”

“…ML, a subset of artificial intelligence, involves the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. − Instead, these systems learn from and make predictions or decisions based on data. This technology has become increasingly pivotal in various sectors, including energy, materials, and biotechnology. − The integration of ML offers a pioneering approach, potentially overcoming these barriers by optimizing biopolymer characteristics and their applications in energy, fuel, and biochemical domains. − However, a substantial research gap persists in the comprehensive understanding and effective deployment of this synergistic relationship.…”

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

“…The convergence of ML, 3D printing, and biopolymers derived from algae is a cutting-edge field brimming with opportunities [37,[108][109][110]. It paves the way for novel applications across various industries and poses significant challenges.…”

“…The optimization of neural network architectures in the field of 3D printing has been a subject of ongoing research, with a focus on creating more adaptable and efficient models. The shift towards using biopolymers derived from natural sources like algae brings its unique set of challenges [110], with Liu et al such as suboptimal processability, solubility issues, and suitable solvents [31]. While the scope for their use in food and biomedical applications is substantial, industry-specific challenges exist, such as the limited variety of 3D-printed foods and the difficulties in implementing biomimicry in the biomedical field.…”