Data-derived soft-sensors for biological wastewater treatment plants: An overview

Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network

“…Recently, Haimi et al (2013) presented a comprehensive review on the use of soft sensors in biological wastewater treatment plant and pointed out that supervised and unsupervised ANN are typically very popular in this field of applications. The trend is confirmed also by the more recently contributions.…”

“…software sensors enables the on-line prediction by returning the primary outputs only when the secondary inputs are available, even in case of noisy measurements (Haimi et al 2013).…”

“…In fact, some of these are generally determined off-line in laboratory under high uncertainty, making an operational limitation (Chapman 1992). The use of software sensors enables the estimation of such off-line primary variable throughout the use of easy on-line measured secondary variables, and has been applied to wastewater treatment plants with success (Haimi et al 2013). Based the modelling approach, the models used for describing the biological processes in the wastewater treatment can be mainly classified as phenomenological and or data-driven models.…”

Comprehensive approach to modeling and simulation of dynamic soft-sensing design for real-time building energy consumption

“…; load PðtÀi + 1Þ Þ + f 2 ðT t ; R t ; H t Þ ð13Þ Soft-sensing technology based on timefrequency decomposition Soft sensors are predictive mathematical models that infer the values of a given process variable from measurements of other variables. 34,35 Because the room thermal load cannot be measured directly, the parameter identification for the soft-sensing model cannot be realized with the sample data. If this bottleneck cannot be solved, room CL soft sensing is difficult to realize.…”

“…[1][2][3] There has been much research on climate change and comfort standards, among which Kwok and Rajkovich 4 showed that the building sector thermal load accounted for 38.92% of the total primary energy requirements (PER) of the United States, of which 34.9% was used for building energy consumption. In China, building sector thermal load accounted for approximately 24.11% of national energy use in 1996, rising to 27.52% in 2001, and is estimated to increase to approximately 35.12% in 2020. 5,6 Although carbon emissions per capita in China are lower than those in other developing countries, its total emissions are the second only to the United States.…”