Modeling the Reaction
The Aqueduct platform provides the ability to simulate the reaction process before running it with real reagents and equipment. This simulation capability is beneficial for validating control algorithms and optimizing their performance.
Constant Rate Kinetic Decay (Smart Dose)
In the Smart Dose control algorithm, the decay in pH rate-of-change over time is modeled using a linear expression. Initially, the rate of pH decrease (dpH/dt) is large and negative. As the reaction progresses, the rate of pH change becomes smaller but remains negative. This behavior can be described by the following expression:
dpH_dt(t) = ROC_start + (t_now - t_start) * delta_ROC_dt
Where:
-
dpH_dt: Rate of change in pH as a function of time. -
ROC_start: Rate of change in pH at the start of the reaction (t_start). -
t_now: Current time. -
t_start: Reaction start time. -
delta_ROC_dt: Decay in reaction kinetics over time.
The ReactionModel class in the models.py file encapsulates this behavior.
Base Addition Rate Dependent (PID)
The PID control algorithm models the pH rate-of-change as a function of the base addition rate. As the reaction progresses, the sensitivity of pH rate-of-change to the base addition rate increases. The behavior can be described by the following expression:
dpH_dt(t, base_addition_rate) = ROC_offset + (ROC_init + (t_now - t_start) * delta_ROC_dt) * base_addition_rate
Where:
dpH_dt: Rate of change in pH as a function of time and base addition rate.ROC_offset: Intrinsic rate of change in pH due to the reaction.ROC_init: Rate of change in pH at the start of the reaction (t_start).t_now: Current time.t_start: Reaction start time.delta_ROC_dt: Decay in reaction kinetics over time.base_addition_rate: Rate of base addition in mL/min.
The PidModel class in the models.py file encapsulates this behavior.