Industrial Mixer
Last updated
Last updated
This tutorial will take you through the process of building agents for a realistic chemical manufacturing use case.
Learn about the use case (this page)
Experiment with your own designs
Mixing and blending tasks are great use cases for intelligent agent systems because they are both complex and high-value. In the example in this tutorial, process improvements could lead to millions in ROI.
In the industrial mixer use case, raw materials are stirred together inside a tank, undergoing a reaction that produces the desired end product.
The goal of the process is to convert as much of the raw material as possible. But as the chemicals mix and the conversion occurs, the tank heats up. If the temperature gets too high, a condition called “thermal runaway” occurs, potentially causing explosions and fires.
To produce as much chemical as possible, the operator must constantly adjust the temperature in the tank, keeping it high enough to allow productivity but low enough to avoid any thermal runaway risk.
As in all machine teaching use cases, this process can be summarized in the form of a goal (maximize yield) and a constraint (avoid thermal runaway) that must be balanced against each other:
The process is controlled by adjusting the mixture's temperature in the tank using a "jacket" filled with coolant. Lowering the coolant temperature in the jacket lowers the temperature in the tank, decreasing the risk of thermal runaway.
However, cooling the tank can also reduce yield. By how much? The answer varies unpredictably – temperature changes affect chemical concentration differently at different parts of the reaction. That nonlinear relationship between temperature and yield is why this is a nuanced process that benefits so much from intelligent automation.
The industrial mixer simulator uses principles of chemistry to model the behavior of the system in real life.
The current automation solution is a linear MPC controller. As the current solution, this agent system's performance is the benchmark for the other designs.
The image below shows an MPC controller represented in the visual system of agent designs. The agent takes in sensor information about the temperature in the tank and the concentrations of the chemicals.
It passes that information to the skills layer of the agent system. The skills layer contains a single programmed skill agent: control reactor. This skill uses a mathematical model to determine the desired temperature set point for the tank. It also determines the control actions to take to achieve that temperature using the cooling jacket, and outputs those actions as decisions.
In simulation, this agent's conversion rate was 82%. That means that 82% of the reagents were turned into product, with 18% waste, but the risk of thermal runaway is high.
Like all technologies, model predictive control has a “personality,” a unique set of strengths and weaknesses that can be seen reflected in this performance. MPC is a rule follower. It is a way of controlling a process using mathematical relationships that can be fully mapped and programmed by human engineers. It works well in situations that are straightforward and linear.
The agent does a good job in the first steady state at the start. However, as the transition phase begins, the agent’s performance starts to fail. The wide shadow shows that this agent can’t adapt to the unpredictable conditions in the transition. Its performance becomes dangerously inconsistent, potentially allowing the temperature to exceed the thermal runaway checkpoint at nearly every point in the reaction.
When an MPC controller is used to control this process in the real world, a human operator needs to step in and take over control before the automated system lets the temperature cross the thermal runaway threshold.
The following tutorials will walk you through the anatomy of some multi-agent systems that take different approaches to learning how to control the temperature of a mixer more effectively to maximize yield and avoid thermal runaway.
We wrote a. Read the white paper to learn about the problem, the agent system designs, and agent system performance. Use this tutorial to build and train all the agents for yourself.
