"In-depth! The core reasons, basic methods, and future trends of 'big data + AI + bioreaction'"

Addressing the uncontrollable challenges of traditional fermentation

Biological fermentation has long relied on manual experience to adjust parameters such as temperature, pH, and feed rate, but the dynamic complexity of cellular metabolism leads to large process fluctuations and multiple abnormal batches.

Real time decision-making achieved through multi-source data fusion

By combining advanced sensing technologies such as online Raman spectroscopy and NIR with AI algorithms, a process analysis technology (PAT) closed-loop system is formed.

For example, a certain university combines AI prediction with robot automation: AI screening for potential enzyme mutation combinations → robot synthesis and testing → data feedback optimization model. 

This process increases feed enzyme activity by 26 times and industrial catalytic enzyme selectivity by 90 times, shortening the traditional protein engineering cycle from several years to just a few weeks.

Cost reduction and efficiency improvement: The AI control system reduces overall costs by about 10%, mainly due to a decrease in energy consumption, an increase in raw material utilization, and a reduction in abnormal batches.

Continuous process enhancement: AI driven continuous biopharmaceutical processes (such as perfusion culture) reduce buffer consumption by 30% and shorten production cycles by 20% by adjusting chromatography column loading in real-time.

A certain university has built a high-throughput biological data analysis platform, which compresses the rice breeding cycle from 8-10 years to 3-5 years through gene mapping and AI breeding program design.

1. Hardware level: By deploying multi-dimensional sensors (such as temperature, pH value, dissolved oxygen, material flow rate, etc.), hundreds of dynamic parameter paths during the production process can be collected in real time.

The sensor network covers key production equipment such as fermentation tanks, reaction vessels, etc., ensuring real-time mapping between the physical world and the digital world.

2. Software level: Build an "AI intelligent brain" based on transfer learning and physical mechanism models, integrate historical production data (such as process parameters), and construct an algorithm framework with predictive and decision-making capabilities.

1. Full cycle time series prediction: Taking the "time dimension" as the core variable, AI generates the optimal operation plan for the future full cycle (such as fermentation process from the 20th hour to the 150th hour) at a millisecond speed based on the current production status, covering material ratio, temperature control, ventilation adjustment and other links.

2. Real time feedback and regulation: By continuously receiving sensor data, dynamically correcting the prediction model, predicting production trends and potential risks in advance, achieving a closed-loop control of "prediction execution feedback optimization", and minimizing production errors.

1. Small sample learning and transfer learning: Breaking through the traditional AI's dependence on massive data, such as requiring only 5% of traditional data, combined with transfer learning technology, quickly adapting to new production scenarios, and reducing model training costs.

2. Physical mechanism empowerment algorithm: By embedding the physical laws of the production process (such as microbial growth kinetics and chemical reaction thermodynamics), the "algorithm black box" problem is reduced, making AI decisions have causal logic interpretability, and easy for operators to understand and intervene.

1. The transformation logic of "model defined production": upgrading the traditional "process standardization" production mode to a "dynamic intelligence" mode through AI, with the core being the real-time optimization of production parameters driven by algorithm models, rather than fixed process execution.

2. Dual improvement of production capacity and stability: AI accurately regulates the growth environment or production process parameters of microorganisms, fully unleashing the potential of production capacity (such as increasing yield by 10% -20%), while ensuring product stability and reducing the risk of fluctuations caused by manual intervention.