Process Data Collection, Analysis, and Modeling for Lab, Pilot, and Production Plants

Understanding material behavior is critical element of identifying viable chemical & process routes, catalysts, and operating conditions necessary to facilitate and control the chemical & physical transformations employed to safely, consistently, and sustainably convert raw materials into desired products.  We rely on measured process data, analytical results, and model output results to inform process & technology development and enable process system operation.  The process insights and understandings obtained from these sources are essential to informed tactical operating decisions, targeted innovation, and business & technology strategy.  Correct interpretation of this information has the potential to open the door to improved understating of the desired physical & material system; however, inferring behavior from erroneous data, incorrect results, and physically impossible model output offers consequences that range from wasted time to catastrophic disaster.  Assessing the quality of process data, analytical, and simulation results and recognizing the differences between good & accurate information providing previously unrealized insight(s) and output from damaged instruments, corrupted signals, contaminated samples, and poor or missing material property system interaction parameters presents a continuing challenge for the process development and operating teams.  Presentations in this session will discuss the collection, interpretation, use, and impacts of process data, analysis, and process modeling results to inform process & technology development and process operation in the laboratory, at pilot scale, and in production plants.