ABSTRACT
This chapter examines reaction optimization and shows how the expansion of its adaptation into the biological sciences can expedite data collection and analysis. Software applications vary greatly from informatics to database management to intelligent automation. There have been major developments in laboratory automation stemming from the emergence of systems capable of processing large numbers of samples in parallel. The range of laboratory automation can be broken into two levels of user input: open-loop and closed-loop experimentation. Diverse automation systems, ranging from batch reactors to multireactor workstations, have been constructed with the dominant application of performing reaction optimization. Adaptive algorithms are the foundation of closed-loop experimentation. Algorithms that can make scientist-independent decisions have made great strides toward intelligent automation. There are many solutions to intelligent automation analysis, but the utility of these solutions can be disputed based solely on their ease of use for a scientist.
