First, welcome to the community. I have some thoughts on the questions you pose, but to be honest, you have not provided enough information regarding the actual situation for me to provide most appropriate advice. For example, you don't describe the response variable.
My thoughts:
1. RSM is meant to "map" the design space so you may select optimum conditions for the selected response variable. It should be done after you have thorough knowledge regarding which, of the few, factors have a significant effect on the response and more importantly, you understand the effect of noise (factors you are not willing to manage or control). You should already have a first order model...
2. Creating a continuous regression model with a categorical factor is a bit non-sensical. You could create a model for each category, but including the term in the model makes no sense. You don't have an inference between the categories nor could there be any non-linear terms. (it's either this acid or that acid type, there is no continuum)
3. You could hypothetically convert acid type into a continuous variable if you can quantify the differences in the types (e.g., amount of a certain chemical compound), but my guess is your types are hydrochloric vs. sulfuric or nitric or something like that.
Are you sure you need the non-linear terms? Have you predicted what the model should be (based on science, not statistics)? What are your hypotheses? Typically we build models starting with first order and augmenting this through iteration, rather than a one-shot experiment. We experiment to understand, not to pick a winner.
Experimentation is extremely powerful, so have at it.
"All models are wrong, some are useful" G.E.P. Box