## 1 Introduction  
Wastewater treatment plants (WWTPs) are essential for eliminating pollutants from wastewater and converting it into clean effluent discharged in rivers or reused for different applications, including irrigation [1]. As treatment regulations on effluent quality are continuously tightening, the values of primary variables, such as organic matter, ammonia, nitrates, phosphorus and suspended solids, have to strictly conform to the challenging water quality requirements. The operation of WWTPs has proven to be tough and challenging. A detailed description of wastewater treatment processes, as well as the models associated with them, are extremely complicated, exhibit nonlinear behavior and are characterized by a large number of intensely changing variables. The most difficult challenges for WWTP management and control of the operation, aside from establishing the appropriate control system structure and determining optimal values for the operated variables, are the daily, weekly or seasonal influent composition fluctuations. Comparing control systems' performance in different plants is difficult and necessitates the creation of simulation standards and benchmarks [2]. Modeling water treatment processes has become a very useful tool for the design, optimization and automatic control of the WWTP, as the importance of the treatment plants has grown today within the circular economy concept, and they are presently considered water resources recovery facilities. The International Water Association (IWA) is well-known for its vast contributions to discovering solutions and fighting against global water problems. IWA experts have created advanced phenomenon-based models to support the construction and control of sewerage treatment processes as part of the envisioned goal of improving standards for sustainable water management [3]. Activated Sludge Model #1 (ASM1) is the most common of these. It was created by the IWA (formerly IAWQ) to standardize nomenclature and set a milestone in wastewater treatment plant modeling [4]. ASM2, ASM2d and ASM3 have been developed over time with the scope of increasing the capability of revealing the intrinsic behavior of processes that occur within the activated sludge water processing [5]; they are currently being employed for sewage treatment plant design and control and are widely used for estimation or optimization [6]. Benchmark Simulation Model #1 (BSM1) was created to enlarge the ASM1 modeling with a defined plant configuration. It has become a standard tool and method for evaluating the performance of wastewater treatment plants. BSM1 has been expanded to Benchmark Simulation Model #2 (BSM2) as a more comprehensive plant description for long-term evaluation, coupled with extensions to primary sewage treatment plant subunits and processing of sludge by digestion in anaerobic conditions [7]. Both benchmarks have become powerful tools to support the development of various control strategies and the evaluation of their performance [8]. Chemical process monitoring is important for evaluating process performance and improving process efficiency and wastewater quality in wastewater treatment plants. The advantage of using mechanical or statistical mathematical models is to estimate the behavior of main key process parameters in different operating circumstances and, in association with real-time measurements, to use them for process design or operational improvement. Calibrated models are a necessary prerequisite for model-driven control solutions due to their high usage in advanced process automation. Supervision and control of water plant process variables are attained to varying extents and complexity depending on the wastewater inflow, the specificity of the bioreactor configuration, and the actual equipment available at each facility. Successful, accurate monitoring and control of processes requires reliable information on the composition, flow rate and temperature for both influents, recycle flows or effluents.