Disclosed are methods and systems for implementing dynamic control of acoustic fields, which involves the use of machine learning through a Markov Decision Process. The machine learning algorithm monitors an attribute of a system and maintains the attribute within a range by altering a signal sent to an acoustic transducer. The acoustic transducer is configured to alter the attribute of the system. The attribute may be an attribute of the acoustic field produced by the acoustic transducer, or may be an attribute of another element of the system, such as a fluid, a rotor, or a cell mass.

A batching system involves a vessel, a mixer, a pump, an optical sensor, at least forty microprocessor-based mass flow meters and at least forty automated valves each controlled by one of the mass flow meters, wherein each of the at least forty microprocessor-based mass flow meters are coupled to the vessel; and wherein the vessel, the mixer, the pump and the optical sensor are coupled together so as to form a circuit within which Liquid Materials, concurrently introduced into the vessel, will re-circulate until at least one reading from the optical sensor indicates that the materials are fully mixed. A batching method involves concurrently introducing into a vessel, through at least forty microprocessor-controlled mass flow meters, Liquid Materials to be mixed, and repeatedly circulating the Liquid Materials through, in sequence, the vessel, a mixer, a pump, and an optical sensor, until the at Liquid Materials are fully mixed.