The Influence of Optic and Polarographic Dissolved Oxygen Sensors Estimating the Volumetric Oxygen Mass Transfer Coefficient (KLa)

Abstract

Aeration is usually the most energy intensive part of the wastewater treatment process. Optimizing the aeration system is essential for reducing energy costs. Field tests oriented to estimate parameters related to oxygen transfer are a common approach to compare aeration systems. The aim of this research is to assess the effect of dissolved oxygen probe lag on oxygen transfer parameter estimation. Experimental procedures regarding to process automation and control were applied to quantify dissolved oxygen probe lag. We have measured oxygen transfer in clean water, under a wide range of conditions (airflow rate, diffuser characteristics and diffuser density), with optic and polarographic sensors for dissolved oxygen measurement. The oxygen transfer was measured as per ASCE Standard procedures. Nonparametric statistical tests were used to compare the estimated volumetric mass transfer coefficient K L a with different sensors. According to the results, there is not significant influence of the probe lag (also known as time constant) or probe characteristics on the parameters used to assess oxygen transfer efficiency. This fact has great relevance in common practice of aerobic process for wastewater treatment because dissolved oxygen monitoring is used as an input for decision making related to the energy optimization in the aeration system. Findings from these tests contradict previous studies which claim that lag time in polarographic sensors for the dissolved oxygen measurement can bias estimate K L a.

Automated End-to-End Workflow for Volumetric Mass-Transfer Coefficient (kLa) Characterization in Small-Molecule Pharmaceutical Development

“…This time lag and sensor response are well studied and accepted to follow first-order kinetics according to eq − where τ is the electrode time constant. C O 2 , e l e c t r o d e = C O 2 , l i q true( 1 − exp ( − t τ ) true) Many models to regress k L a assume that sensor lag is the main source of lag and error in the system and assume that other effects are negligible. These methods recommend data truncation to remove all data points that may be influenced by system startup or deaeration procedures. ,, Other sources of error and measurement delay that are typically ignored include the start-up time of the agitator, the misalignment of manual operations (e.g., start of gassing and start of agitation), lag introduced from dissolved oxygen probe placement, lingering deaeration effects, or other error sources originating from an imperfect oxygen step change in either phase.…”