Software sensors based on titrimetric techniques for the monitoring and control of aerobic and anaerobic bioreactors

Biokinetic aspects for biocatalytic remediation of xenobiotics polluted seawater

AIMS

This research was conducted to investigate the biocatalytic remediation of xenobiotics polluted seawater using two biocatalysts; whole bacterial cells of facultative aerobic halotolerant Corynebacterium variabilis Sh42 and its extracted crude enzymes.

METHODS AND RESULTS

One-Factor-at-A-Time technique and statistical analysis were applied to study the effect of initial substrate concentrations, pH, temperature, and initial biocatalyst concentrations on the batch biocatalytic degradation of three xenobiotic pollutants (2-hydroxybiphenyl (2-HBP), catechol and benzoic acid) in artificial seawater (salinity 3·1%). HPLC and gas-chromatography mass spectroscopy analyses were utilized to illustrate the quantitative removal of the studied aromatic xenobiotic pollutants and their catabolic pathway. The results revealed that the microbial and enzymatic cultures followed substrate inhibition kinetics. Yano and Koga's equation showed the best fit for the biokinetic degradation rates of 2-HBP and benzoic acid, whereas Haldane biokinetic model adequately expressed the specific biodegradation rate of catechol. The biokinetic results indicated the good efficiency and tolerance of crude enzyme for biocatalytic degradation of extremely high concentrations of aromatic pollutants than whole C. variabilis Sh42 cells. The monitored by-products indicated that the catabolic degradation pathway followed an oxidation mechanism via a site-specific monooxygenase enzyme. Benzoic acid and catechol were identified as major intermediates in the biodegradation pathway of 2-HBP, which were then biodegraded through meta-cleavage to 2-hydroxymuconic semialdehyde. With time elapsed, the semialdehyde product was further biodegraded to acetaldehyde and pyruvic acid, which would be further metabolized via the bacterial TCA cycle.

CONCLUSION

The batch enzymatic bioreactors performed superior-specific biocatalytic degradation rates for all the studied xenobiotic pollutants.

SIGNIFICANCE AND IMPACT OF THE STUDY

The enzymatic system of C. variabilis Sh42 is tolerable for toxic xenobiotics and different physicochemical environmental parameters. Thus, it can be recommended as an effective biocatalyst for biocatalytic remediation of xenobiotics polluted seawater.

Development of software sensors for determining total phosphorus and total nitrogen in waters

Total nitrogen (TN) and total phosphorus (TP) concentrations are important parameters to assess the quality of water bodies and are used as criteria to regulate the water quality of the effluent from a wastewater treatment plant (WWTP) in Korea. Therefore, continuous monitoring of TN and TP using in situ instruments is conducted nationwide in Korea. However, most in situ instruments in the market are expensive and require a time-consuming sample pretreatment step, which hinders the widespread use of in situ TN and TP monitoring. In this study, therefore, software sensors based on multiple-regression with a few easily in situ measurable water quality parameters were applied to estimate the TN and TP concentrations in a stream, a lake, combined sewer overflows (CSOs), and WWTP effluent. In general, the developed software sensors predicted TN and TP concentrations of the WWTP effluent and CSOs reasonably well. However, they showed relatively lower predictability for TN and TP concentrations of stream and lake waters, possibly because the water quality of stream and lake waters is more variable than that of WWTP effluent or CSOs.

Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters

In this study two approaches to predict the total alkalinity (expressed as mg L(-1)HCO(3)(-)) of an anaerobic digester are examined: firstly, software sensors based on multiple linear regression algorithms using data from pH, redox potential and electrical conductivity and secondly, near infrared reflectance spectroscopy (NIRS). Of the software sensors, the model using data from all three probes but a smaller dataset using total alkalinity values below 6000 mg L(-1)HCO(3)(-) produced the best calibration model (R(2)=0.76 and root mean square error of prediction (RMSEP) of 969 mg L(-1)HCO(3)(-)). When validated with new data, the NIRS method produced the best model (R(2)=0.87 RMSEP=1230 mg L(-1)HCO(3)(-)). The NIRS sensor correlated better with new data (R(2)=0.54). In conclusion, this study has developed new and improved algorithms for monitoring total alkalinity within anaerobic digestion systems which will facilitate real-time optimisation of methane production.