Let's start with how the response data was actually collected because in large measure this will help determine the suggested analysis pathways. Was the data collected in the context of a designed experiment? You cited using Python rather than JMP to design the experiment. Not sure what that means. How was 'reactor' treated as a factor type in the design, if treated at all? Outright classification factor unto itself? Or a blocking factor? What other nuisance variables might be in play between the reactors? For example, were the same raw materials used in each reactor? We used to run experiments on a 'reactor' half a world away from the other, with the idea of trying to compare the 'reactor' effect...and there was no way we had identical raw materials, let alone, operators, measurement systems and on and on.
Was the data 'happenstance data'? In other words, did you just collect manufacturing data from multiple process runs and are now trying to torture information/insight out of it? Commonplace in manufacturing data...hey, the data is almost 'free'! We might as well try and see what we can see!
All these issues will lead to selection of appropriate analysis pathways. But regardless of the answers to the above questions...my advice wrt to analysis is start with simple visualizations of the data that help answer the practical questions at hand. There is always the temptation to jump right to modeling of some sort and ignoring/skipping over JMP's more simple visualization platforms like Graph Builder, Distribution, and Fit Y by X.
Last 'ask'...can you share your data set...even if it's anonymized?
