Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

본 논문은 회분식 반응기에서 습식 산화법으로 합성한 칼륨 페레이트(VI)에 의한 난분해성 아조 염료Reactive Black 5의 분해 과정을 연구하는 것을 목적으로 한다. 수용액에서 RB5의 분해는 pH, Ferrate (VI) 투입량, 초기 농도, 수용액 온도 등 다양한 변수의 조건에서 연구되었다. RB5 경우에는 최대 분해 효율은 pH 7.0에서 63.2%가 달성되었으며, 이 실험 조건에서 얻은 k_app 값은 190.49 M^-1s^-1으로 나타났다. 온도 또한 가장 중요한 매개 변수 중 하나로 연구되었으며, 그 결과로부터 온도(45℃까지)를 증가시키면 페레이트(VI)에 의한 아조 화합물 염료의 분해 효율이 증가하고, 온도가 45℃를 초과하면 분해 효율이 저하되는 것으로 나타났다.

Wastewater management is increasingly emphasizing economic and environmental sustainability. Traditional methods in sewage treatment plants have significant implications for the environment and the economy due to power and chemical consumption, and sludge generation. To address these challenges, a study was conducted to develop the Intermittent Cycle Extended Aeration System (ICEAS). This approach was implemented as the primary technique in a full-scale wastewater treatment facility, utilizing key operational factors within the standard Sequencing Batch Reactor (SBR) process. The optimal operational approach, identified in this study, was put into practice at the research facility from January 2020 to December 2022. By implementing management strategies within the biological reactor, it was shown that maintaining and reducing chemical quantities, sludge generation, power consumption, and related costs could yield economic benefits. Moreover, adapting operations to influent characteristics and seasonal conditions allowed for efficient blower operation, reducing unnecessary electricity consumption and ensuring proper dissolved oxygen levels. Despite annual increases in influent flow rate and concentration, this study demonstrated the ability to maintain and reduce sludge production, electricity consumption, and chemical usage. Additionally, systematic responses to emergencies and abnormal situations significantly contributed to economic, technical, and environmental benefits.