A wastewater treatment configuration consisting of advanced oxidation pretreatment and biological wastewater treatment process (BWTP) was investigated to treat a reclaimer wastewater generated in a steel-making industry, which contained high concentration MDEA (N-methyldiethanolamine) of up to 20,548 mg/L and other pollutants such as formate, phenol, and thiocyanate. The Fenton, ozone, and peroxone methods were tested as candidates, and the peroxone method was chosen because it could selectively decompose MDEA resulting in the final MDEA and chemical oxygen demand (COD) removal efficiencies of 92.87% and 27.16%, respectively. Through the respirometer tests using the sludge of the BWTP, it was identified that the microbial toxicity of the peroxone-pretreated wastewater was negligible and the short-term biochemical oxygen demand (BOD) to COD ratio, indicating that the biodegradability of wastewater significantly increased from 0.103 to 0.147 by the peroxone pretreatment. Analysis of the oxygen uptake rate profiles also revealed that the microbial degradation rate of the pollutants present in the reclaimer wastewater was in the order of phenol > formate > thiocyanate > MDEA, which could be changed depending on the microbial community structure of the BWTP.

A moving window decision-making algorithm is proposed for the cleaning schedule optimization of heat exchanger network system subject to fouling in refinery crude preheat train. This algorithm is designed by incorporating the moving window scheme into a conventional multi-period optimal control problem (OCP) framework and has a distinct feature that it can efficiently handle a complex problem where a long-time horizon is considered. When compared with the conventional multi-period OCP method using fixed time horizon, our algorithm always shows an excellent performance regarding the computational time, still finding a compatible optimal solution. In our moving window decision-making algorithm, it is important to determine the optimal moving window size for the given time horizon as it significantly influences the optimization performance.

No abstract