自动化和人工智能（AI）在真菌生物加工中的整合，标志着生物制造的重大进步，特别是在通过循环经济原则实现可持续发展目标方面。丝状真菌具有非凡的代谢多功能性，使其成为将有机基质转化为有价值的生物产品的理想候选者。自动化用机械化工具取代人工任务，优化了工艺效率并减少了人为错误。另一方面，AI利用数据洞察力赋予这些系统预测分析和实时决策能力，从而增强了过程控制和资源利用率。这种协同作用使真菌能够生产各种生物产品，例如酶、有机酸和生物活性化合物，为从制药到食品技术等各个领域做出贡献。

🍄 **自动化和AI在真菌生物加工中的整合**：自动化用机械化工具取代人工任务，优化了工艺效率并减少了人为错误。AI利用数据洞察力赋予这些系统预测分析和实时决策能力，从而增强了过程控制和资源利用率。这种协同作用使真菌能够生产各种生物产品，例如酶、有机酸和生物活性化合物，为从制药到食品技术等各个领域做出贡献。

🤖 **智能生物反应器在真菌培养中的应用**：智能生物反应器配备传感器和执行器，确保在浸没式发酵（SmF）和固态发酵（SSF）系统中对真菌生长动力学进行精确监控和控制。这种技术整合解决了氧气传递限制和热量积累等关键挑战，这些挑战传统上阻碍了可扩展性。通过利用工业4.0原则，生物制造部门可以实现自主运行，优化生产产量并最大限度地减少环境影响。

🧠 **AI驱动的工具和系统在丝状真菌培养中的应用**：利用AI驱动的工具和系统对于优化生物过程至关重要，可以通过最大限度地提高产品产量和最小化成本和环境影响来实现。通过AI实现的自动化促进了对pH、温度和营养水平等关键参数的实时监控和控制。智能传感器能够进行原位采样，提供连续数据而不会破坏无菌性。图像分析工具自动执行生物量测量和真菌形态评估，提高效率和准确性。机器人系统处理营养添加和采样等复杂任务。智能生物反应器将AI集成到高级过程控制中，提高了可扩展性和可重复性。这些技术有望通过确保一致的高质量生产成果来彻底改变真菌生物加工。

🧬 **未来方向和研究需求**：未来真菌生物加工的进步应侧重于整合AI和自动化，以增强实时数据收集，优化有机酸、酶和药物的生产，并提高运营效率。开发多参数智能传感器以简化监控和控制至关重要，从而减少安装复杂性和污染风险。此外，在自动化形态控制、在线生物量估算和质量控制方面的进步对于有效扩展生物过程至关重要。解决这些挑战将支持可持续的粮食生产，并在气候和资源限制中满足不断增长的全球需求，从而推动更有效和更具成本效益的生物加工解决方案。

🔍 **研究方向**：未来研究应侧重于开发更先进的AI算法，以优化真菌生长条件、提高产品产量并降低生产成本。此外，研究人员应探索AI在生物过程监控和控制中的应用，以确保可扩展性和可重复性。通过整合AI和自动化，真菌生物加工可以实现更大的可持续性，并为生物制造业做出重大贡献。

Automation and AI in Fungi-Based Bioprocesses: Advancing Towards Sustainable Biomanufacturing:

Integrating automation and AI in fungi-based bioprocesses marks a significant advancement in biomanufacturing, particularly in achieving sustainability goals through circular economy principles. Filamentous fungi possess remarkable metabolic versatility, making them ideal candidates for converting organic substrates into valuable bioproducts. Automation replaces manual tasks with mechanized tools, optimizing process efficiency and reducing human error. Conversely, AI empowers these systems with predictive analytics and real-time decision-making capabilities based on data insights, enhancing process control and resource utilization. This synergy enables fungi to produce diverse bioproducts such as enzymes, organic acids, and bioactive compounds, contributing to sectors ranging from pharmaceuticals to food technology.

The application of smart bioreactors equipped with sensors and actuators ensures precise monitoring and control of fungal growth dynamics in both submerged fermentation (SmF) and solid-state fermentation (SSF) systems. This technological integration addresses critical challenges like oxygen transfer limitations and heat buildup, which traditionally hindered scalability. By leveraging Industry 4.0 principles, biomanufacturing sectors can achieve autonomous operation, optimizing production yields and minimizing environmental impact. Despite these advancements, further research is needed to fully exploit AI’s potential in optimizing nutrient utilization and product yield in fungi-based bioprocesses, particularly in the context of food production, thus bridging existing knowledge gaps for future sustainable innovations.

Basics of Automation, Artificial Intelligence, and Machine Learning: 

Automation in industrial biotechnology involves replacing manual tasks with mechanized tools to enhance process control and optimization, thereby reducing human error and contamination risks. AI simulates human cognitive abilities, enabling machines to make autonomous decisions based on data analysis. It encompasses supervised, unsupervised, semi-supervised, and reinforcement learning methods, crucial for optimizing bioprocesses by improving productivity and ensuring regulatory compliance. Robots, integral to automation, perform repetitive or hazardous tasks with precision and efficiency, contributing to enhanced data acquisition and process reliability.

AI-Based Tools and Systems in Filamentous Fungi Cultivation:

In filamentous fungi cultivation, leveraging AI-driven tools and systems is crucial for optimizing bioprocesses by maximizing product yields and minimizing costs and environmental impacts. Automation through AI facilitates real-time monitoring and control of critical parameters like pH, temperature, and nutrient levels. Smart sensors enable in situ sampling, providing continuous data without disrupting sterility. Image analysis tools automate biomass measurement and fungal morphology assessment, enhancing efficiency and accuracy. Robotic systems handle complex tasks such as nutrient addition and sampling. Smart bioreactors integrate AI for advanced process control, improving scalability and reproducibility. These technologies promise to revolutionize fungal bioprocessing by ensuring consistent, high-quality production outcomes.

Automated Estimation of Water Activity in Solid-State Fermentation:

In SSF, where fungi thrive with minimal free water, accurately estimating water activity (aw) is crucial for optimizing growth conditions. A method was devised using MATLAB to estimate surface condensation, a proxy for aw, based on digital image analysis of fungal biomass and water droplets. This non-destructive approach offers a cost-effective means to monitor and control fermentation parameters, ensuring optimal fungal growth and metabolic activity. Such advancements enhance process efficiency and mitigate contamination risks, underscoring the role of AI-driven tools in advancing SSF bioprocessing.

Research Needs and Future Directions in Fungi-Based Bioprocesses:

Future advancements in fungi-based bioprocesses should focus on integrating AI and automation to enhance real-time data collection, optimize the production of organic acids, enzymes, and pharmaceuticals, and improve operational efficiency. Developing multi-parameter smart sensors to streamline monitoring and control is critical, reducing installation complexity and contamination risks. Additionally, advancements in automated morphology control, online biomass estimation, and quality control are essential for scaling up bioprocesses effectively. Addressing these challenges will support sustainable food production and meet rising global demands amidst climate and resource constraints, driving towards more efficient and cost-effective bioprocessing solutions.

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Sources:

https://www.researchgate.net/publication/365880441_Automation_and_artificial_intelligence_in_filamentous_fungi-based_bioprocesses_A_review

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