Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration

Abstract

A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibration of soft sensors based on multiway principal component analysis, a similarity analysis, and robust, generalist phase detection in multiphase bioprocesses. This generalist soft sensor concept was evaluated in two multiphase bioprocesses with various target values, media, and microorganisms. Consequently, the soft sensor concept was tested for biomass prediction in a Pichia pastoris process, and biomass and protein prediction in a Bacillus subtilis process, where the process characteristics (cultivation media and cultivation strategy) were varied. High prediction performance was demonstrated for P. pastoris processes (relative error = 6.9%) as well as B. subtilis processes in two different media during batch and fed-batch phases (relative errors in optimized high-performance medium: biomass prediction = 12.2%, protein prediction = 7.2%; relative errors in standard medium: biomass prediction = 12.8%, protein prediction = 8.8%).

Modeling and Optimization of an Enhanced Soft Sensor for the Fermentation Process of Pichia pastoris

“…Under stable working conditions, traditional soft sensor models demonstrate robust predictive performance [26,27]. However, as industrial demands evolve, the working conditions for the fermentation process of Pichia pastoris are subject to change, leading to a noticeable increase in the predictive error of conventional models.…”

Data synchronization techniques and their impact on the prediction performance of automated recalibrated soft sensors in bioprocesses

“…As a basic prediction model, a linear model with all process variables available online (pO 2 , pH, temperature, addition of pH correcting agents, addition of substrate, CO 2 and O 2 concentration in the exhaust gas), as well as additionally calculated variables (CER and OUR, as well as the cumulative values of these variables) as input were used. This underlying linear model structure has already been successfully used for several bioprocesses [2], including P. pastoris and B. subtilis [14]. The structure of the algorithm used to recalibrate this soft sensor model is described below.…”

“…As mentioned, the prediction is recalibrated in five fixed sections per process. These prediction windows could be adapted to the process phases by, e.g., automated phase detection [14,[38][39][40]. Thus, the underlying relationships between variables in phase-dependent selected process sections do not change, and better prediction models are formed.…”

“…In many applications, soft sensors are still recalibrated manually, which is a very time-consuming and expensive process [10]. However, the automatic recalibration of soft sensors is an alternative and subject of different studies and is referred to as just-in-time modeling [11][12][13][14][15]. The approach is usually similar: First, a time is defined, or, depending on process conditions, it is determined when a recalibration should occur.…”

“…All these steps can be carried out automatically using an optional manual specification of initial conditions. The selection can be based on the chronological order (temporal similarity criterion) [15] of the data sets as well as spatial similarity (distance-based similarity criterion) [11][12][13][14]. Finally, the soft sensor model is recalibrated based on the selected data sets and defined as valid until the subsequent recalibration.…”