Control of Specific Growth Rate in Fed-Batch Bioprocesses: Novel Controller Design for Improved Noise Management

Abstract

Accurate control of the specific growth rate (µ) of microorganisms is dependent on the ability to quantify the evolution of biomass reliably in real time. Biomass concentration can be monitored online using various tools and methods, but the obtained signal is often very noisy and unstable, leading to inaccuracies in the estimation of μ. Furthermore, controlling the growth rate is challenging as the process evolves nonlinearly and is subject to unpredictable disturbances originating from the culture’s metabolism. In this work, a novel feedforward-feedback controller logic is presented to counter the problem of noise and oscillations in the control variable and to address the exponential growth dynamics more effectively. The controller was tested on fed-batch cultures of Kluyveromyces marxianus, during which μ was estimated in real time from online biomass concentration measurements obtained with dielectric spectroscopy. It is shown that the specific growth rate can be maintained at different setpoint values with an average root mean square control error of 23 ± 6%.

Real-time monitoring and control for high-efficiency autonomous laser fabrication of silicon nanoparticle colloids

“…The drawback of the continuous flow system is a reduction in SiNps concentration in the colloid to 6.8 from 73 mg/L for the batch process. However, 6.8 mg/L is an acceptable concentration for most biomedical applications such as DNA tagging [28], allowing Nps produced in this way to be used in single-step functionalisation for biomarker development.…”

“…For LASiS to become an accepted Np production process in regulated environments, such as the biopharma industry, it must meet regulations such as the FDA Process Analytical Technology (PAT) guidelines and QbD frameworks. A major component of the QbD framework is to monitor the critical process parameters in a timely fashion and enact process change in order to ensure the process stays within specifications and control [10,28].…”

Application of In-Situ and Soft-Sensors for Estimation of Recombinant P. pastoris GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut+) and HBsAg (MutS) Production Processes under Varying Conditions

“…A common characteristic of in-situ turbidity and permittivity measurements is a cross-sensitivity to the stirring rate, bubble size and their movement near/trough the sensor-measuring tip that were observed or discussed in probe application reports published elsewhere [ 18 , 32 , 33 , 34 , 35 ]. To avoid this uncommon side effect, degassed chambers or signal filtering is suggested.…”

“…To avoid this uncommon side effect, degassed chambers or signal filtering is suggested. Typically such uncommon in-situ turbidity and permittivity sensor shift amplitudes are too high to be avoided or minimized by simple moving average or Savitzky–Golay filtering algorithms [ 33 ] usually useful for smoothing random measurement noise. Instead, the use of methods that validate the sensor signal reading prior to its use for biomass estimation are necessary.…”

Fuzzy Logic-Based Adaptive Control of Specific Growth Rate in Fed-Batch Biotechnological Processes. A Simulation Study

“…The academic community has proposed various PID controller parameter tuning approaches that take into account time-varying operating conditions: rule-based fuzzy systems [8], first-principle models [9], gain scheduling methods [10][11][12], and other techniques [1,2,[13][14][15]. For exponentially evolving processes, a technique based on feedforward-feedback control was proposed [16]. The proposed methods provide a basis for both theory and practice enabling the utilization of adaptive control algorithms in biotechnological process control and highlight that the application of these control methods can add significant value to the performance of basic control systems.…”