By Greg McMillan
Monday morning you get a flood of emails saying that product quality is out the window, and you’re thinking of following it. There is a long line of batch operations between the raw materials and the final product. Lab samples show that product and byproduct endpoint concentrations of the batches have changed. The furthest upstream operation is a batch reactor. Something has changed in the fed batch reaction, but what? The process engineer says the reaction rate depends upon pressure, temperature, pH and concentrations. The data historian has no concentration measurements. A review of the trend charts shows that the pressure, temperature and pH are tightly controlled at their setpoints. You could probably tune the loops faster, but what is the point? The supplier spec sheets on the latest raw material shipments state everything is per the purchase orders. Is it time to retire to a tropical beach or pursue the management route so you can delegate the problem?
You are intrigued by the mystery so you start to look for clues. You reason that the control loops are doing a great job of transferring variability from the controlled variables (temperature, pressure and pH) to the manipulated variables (coolant, gas and liquid reactant, and reagent flows). The controller outputs appear much more interesting. The batch profiles of coolant, reactants and reagents flows show that their peaks are lower, offset and longer. The reagent profile also shows a long tail in the reagent flow. Why?
The “in-place” standardizations of the pH electrode indicate an offset between the pH at the start and end of the batch. A check with maintenance reveals a new electrode was installed at the start of the batch, and the reference electrode had not reached equilibrium with the process, which had a higher temperature and ionic strength than the buffers. The gradual change in the reference junction potential caused the long tail in the reagent flow. However, this tail is just part of the story. What caused the change in the width, location and maximum value of the peaks in the coolant, reactants and reagent flows?
Models are Knowledge
You can’t experiment with the plant because, even if it was not sold out, and the raw material and energy cost was justified, the review and paper work requirements would take too long. You turn to experimentation with a virtual plant. Within a few hours you find that flow profiles are symptomatic of changes in the reactant concentration. The model shows that some of the flows can be used as inferential measurements of reaction rate. The gaseous reactant feed rate under pressure control is proportional to reaction rate, since the pressure loop automatically adds reactant to make up for the reactant consumed. Additionally, you find coolant flow rate under temperature control is indicative of reaction rate because coolant makeup flow is compensating for the heat of reaction.
Making Do With What You Got
The makeup coolant temperature is tightly controlled at its setpoint. Everything points to a change in the raw materials. You can start to do extensive lab testing of each shipment, but that is too late for previous batches unless the problem persists. The ability to correlate lab results to future batches is not clear-cut since the lab analysis takes several hours and is done only during the day shift. The raw material shipments also enter the top of a storage tank, and the reactor is fed from the bottom. The degree of mixing from turbulence and equilibration is small, which creates a big transport delay from top to bottom.
Figure 1. Effect of PID, feed-forward and sample time on glucose concentration control.
The fix you have with the information you’ve got is to get time-stamped samples of each shipment into your data historian so they can be compared to future batch flow profiles on trend charts. A dynamic real-time model of the storage tank concentration with no mixing is added to provide an inferential estimator of reactant component concentrations being fed to the reactor.
Since pressure, temperature and pH affect reaction rate, there is an opportunity for model-predictive control (MPC) to help maintain an inferential measurement of reaction rate at its maximum by the manipulation of these loop setpoints. A virtual plant shows that a prototype of the MPC performs well and leads to a reduction in cycle time.
Inferential measurements and soft sensors eventually need feedback correction, so you request lab tests be done periodically on a batch to provide an online adjustment. You reason that the number of batches you can wait in between special runs of lab analysis is a function of the trend in the corrections needed. Fortunately, in the case of reaction rates, the actual value is not as important as a directional relative indication of the changes in rate, which leads to the question: Is this the time for something much better for batch reactors?
Concentrating on Concentrations
Nearly all chemical and biological reaction rates depend upon the concentrations of the reactants, and quality depends upon the resulting product and byproduct concentrations. Yet you would be pressed to find off-line, let alone at-line or online concentration measurements of any components of reactants, products, byproducts or contaminants during any batch. The best kept secret of batch reactors is the concentration profiles.