Data-driven modeling for wastewater treatment process (WWTP) under hybrid cloud environment, has been widely regarded as a promising solution. Existing methods managed to learn a forward mapping for WWTP, and were highly reliable on rich intermediate process parameters (IPP) such as dissolved oxygen amount. However, they cannot well handle scenes where IPP are unavailable. In fact, such situations are quite common because many wastewater treatment plants still lack relevant monitoring systems. To remedy such gap, this research collected real-world data from wastewater treatment plants to build realistic experimental scenarios. On this foundation, a probabilistic model for WWTP, named Pro-WWTP for short, is proposed in this paper. More concretely, generative processes of outlet results are expressed as conditional probability, and IPP are estimated via Gibbs sampling-based Bayesian posterior probabilistic inference. Empirically, we conduct two groups of experiments to evaluate proactivity of the proposed Pro-WWTP. Experimental results reveal that Pro-WWTP possesses proper recovery precision for IPP and is able to promote modeling efficiency. Besides, another group of experiments are further implemented to verify total robustness of Pro-WWTP.
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