Representative sampling for process analytical characterization of heterogeneous bioslurry systems—a reference study of sampling issues in PAT

Extensive experimentation and evaluation of alternative sampling methods of highly heterogenous bioslurries are carried out in the context of the theory of sampling (TOS) in order to delineate optimal, representative sampling procedures in PAT (process analytical technologies). The analytical methods investigated are at-line NIR and image analysis (IA) for monitoring of an industrial bioenergy anaerobic digestion processes (AD), subject to stringent economic bracketing: PAT solutions have to be both practical and inexpensive in the bioenergy sector where cost efficiency of instrumentation and monitoring systems is an absolute must. Focus is on development of minimum expenditure methods for sufficient characterization of the very heterogeneous types of biomass feedstock as used in industrial scale, continuously stirred tank reactors (CSTR). Product and process characterization necessarily involves a chemometric multivariate calibration predictor (PLS regression). The general goal is development of appropriate sampling/PAT facilities for at-line/on-line process monitoring in typically low-tech bioenergy, agro-industrial sectors. Experimental laboratory reactor evaluations, based on biomass feedstock and digested products from a full-scale biogas plant, were initially run in batch mode for a week, followed by fed-batch addition of maize silage, introducing a systematic increase in total solids allowing properly spanning multivariate calibration models. Measurements on 55 laboratory reactor samples taken during a complete 14-day fermentation cycle included three key process parameters: total solids (TS), volatile solids (VS) and chemical oxygen demand (COD), representing difficult-to-sample analytes. NIR spectroscopy and image analysis, including the angle measure technique transform (AMT), were evaluated for characterization of different feedstocks as well as continuously extracted process samples with respect to selected chemistry and dry matter characteristics. Optimized sampling on four different scale-levels allowed acceptable PLS prediction models for TS and VS for both NIR and image analysis compared to chemical reference analysis, while it was not possible to predict the COD levels satisfactorily due to large uncertainties in mandatory reference measurement protocols. This feasibility study is promising for NIR as at-line prediction of TS and VS content as well as other AD parameters, which can be measured on the same sample types, while image analysis is currently too complex and expensive for these industry sectors. The findings in the present bioslurries study have a considerable generalization potential: all PAT approaches are critically dependent on representative reference calibration sampling, which has to be fully compliant with the theory of sampling (TOS).