The integration of automation and artificial intelligence (AI) in fungi-based bioprocesses is becoming instrumental in achieving sustainability through a circular economy model. These processes take advantage of the metabolic versatility of filamentous fungi, allowing for conversion of organic substances into bioproducts. Automation replaces manual procedures enhancing efficiency, while AI improves decision making and control based on data analysis.
Bioreactors equipped with smart technology ensure precise monitoring of fungal growth dynamics. Both submerged fermentation (SmF) and solid-state fermentation (SSF) systems are benefiting from this technological synergy, addressing issues such as oxygen transfer limitations and heat buildup. Autonomous operation by leveraging Industry 4.0 principles improves production rates and decreases environmental impact. However, more research is required to fully exploit the true potential of automation and AI.
In industrial biotechnological settings, automation involves substituting manual labor with mechanized equipment, thus reducing human error and contamination risks. AI emulates human decision-making capacities, allowing machines to make informed decisions on the basis of data analysis. Digital brains in robots perform repetitive or hazardous tasks with greater precision and productivity, boosting data gathering and ensuring operational reliability.
The use of AI tools in cultivating filamentous fungi is vital for maximizing bioprocessing outcomes while minimizing costs and environmental repercussion. Automation through AI allows real-time observation and controlling of key parameters like pH, temperature, and nutrient levels. Smart sensors allow continuous in situ sampling, while minimizing disruption to the sterile environment. AI-powered tools automate intricate tasks like biomass measuring and morphological assessment of fungi, increasing productivity and precision.
In solid-state fermentation (SSF), where fungi thrive with minimal free water, estimating water activity (aw) accurately is paramount. A non-destructive methodology using MATLAB to estimate the surface condensation, an indicator for aw, has been devised. This approach provides a cost-effective way to keep an eye on and regulate fermentation parameters.
Aside from enhancing process productivity, this approach mitigates the risk of contamination, highlighting the role of AI tools in SSF bioprocessing. The future of fungi-based bioprocesses relies on the integration of automation and AI. Development of multi-parameter smart sensors to monitor and control is crucial. Enhancements in automated morphology control, biomass estimation, and quality control in real-time are also vital for successful upscale bioprocessing.
Addressing existing challenges will support sustainable food production, meeting rising global demands, and moving towards a more cost-effective and efficient bioprocessing solution, amidst climate change and resource constraints.
