Many physical target values in technical processes are error-prone, cumbersome, or expensive to measure automatically. One example of a physical target value is the wort density, which is an important value needed for beer production. This article introduces a system that helps the brewer measure wort density through sensors in order to reduce errors in manual data collection. Instead of a direct measurement of wort density, a method is developed that calculates the density from measured values acquired by inexpensive standard sensors such as pressure or temperature. The model behind the calculation is a neural network, known as LSTM.

In the biopharmaceutical manufacturing, fermentation process plays a critical role impacting on productivity and profit. Since biotherapeutics are manufactured in living cells whose biological mechanisms are complex and have highly variable outputs, in this paper, we introduce a model-based reinforcement learning framework accounting for model risk to support bioprocess online learning and guide the optimal and robust customized stopping policy for fermentation process. Specifically, built on the dynamic mechanisms of protein and impurity generation, we first construct a probabilistic model characterizing the impact of underlying bioprocess stochastic uncertainty on impurity and protein growth rates. Since biopharmaceutical manufacturing often has very limited data during the development and early stage of production, we derive the posterior distribution quantifying the process model risk, and further develop the Bayesian rule based knowledge update to support the online learning on underlying stochastic process. With the prediction risk accounting for both bioprocess stochastic uncertainty and model risk, the proposed reinforcement learning framework can proactively hedge all sources of uncertainties and support the optimal and robust customized decision making. We conduct the structural analysis of optimal policy and study the impact of model risk on the policy selection. We can show that it asymptotically converges to the optimal policy obtained under perfect information of underlying stochastic process. Our case studies demonstrate that the proposed framework can greatly improve the biomanufacturing industrial practice.

The key ingredient, the experts say, is 5G. It gives developers the ability to scale up projects more easily because there's no need to build extensive fiber-optic networks to keep data flowing. What's more, 5G networks let internet-connected devices transmit much more information much more quickly--which in turn is spurring developers to come up with more advanced machines that can take maximum advantage of the capability. "5G in the field, in real-world deployments, enhances the value of all these other technologies," says Bill Menezes, a senior principal analyst at information-technology research and advisory firm Gartner Inc. Here's a look at early examples of what is possible when these technologies are yoked together: In the food industry, AI is already being used to track supply chains and ingredient quality, sort produce and even create taste profiles to target specific demographics. And the technology is poised to take on ever more complex tasks as it links up with 5G and networks of online-capable devices known as the Internet of Things.