Combined sewer networks carry wastewater and stormwater together.Capacity limitation of these sewer networks results in combined sewer overflows(CSOs)during high-intensity storms.Untreated CSOs when directly discharge...Combined sewer networks carry wastewater and stormwater together.Capacity limitation of these sewer networks results in combined sewer overflows(CSOs)during high-intensity storms.Untreated CSOs when directly discharged to the nearby natural water bodies cause many environmental problems.Controlling existing urban sewer networks is one possible way of addressing the issues in urban wastewater systems.However,it is still a challenge,when considering the receiving water quality effects.This paper presents an evolutionary constrained multi-objective optimization approach to control the existing combined sewer networks.The control of online storage tanks was taken into account when controlling the combined sewer network.The developed multi-objective approach considers two important objectives,i.e.the pollution load to the receiving water from CSOs and the cost of the wastewater treatment.The proposed optimization algorithm is applied here to a realistic interceptor sewer system to demonstrate its effectiveness.展开更多
The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors.This study considers the sulfuric acid corrosive factor in wastewate...The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors.This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models.The models include three different types of extreme learning machines,including the standard,online sequential,and kernel extreme learning machines,in addition to the artificial neural network,classification and regression tree model,and statistical multiple linear regression model.The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models.The input variability was assessed based on two scenarios prior to the application of the predictive models.For the first assessment,the machine learning models were developed using all the available cement and concrete mixture input variables;the second assessment was conducted based on the gamma test approach,which is a sensitivity analysis technique.Subsequently,the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches.The adopted methodology attained optimistic and reliable modeling results.The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.展开更多
文摘Combined sewer networks carry wastewater and stormwater together.Capacity limitation of these sewer networks results in combined sewer overflows(CSOs)during high-intensity storms.Untreated CSOs when directly discharged to the nearby natural water bodies cause many environmental problems.Controlling existing urban sewer networks is one possible way of addressing the issues in urban wastewater systems.However,it is still a challenge,when considering the receiving water quality effects.This paper presents an evolutionary constrained multi-objective optimization approach to control the existing combined sewer networks.The control of online storage tanks was taken into account when controlling the combined sewer network.The developed multi-objective approach considers two important objectives,i.e.the pollution load to the receiving water from CSOs and the cost of the wastewater treatment.The proposed optimization algorithm is applied here to a realistic interceptor sewer system to demonstrate its effectiveness.
基金This research was financially supported by the Alexander von Humboldt Foundation within the framework of a Georg Forster Research fellowship.
文摘The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors.This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models.The models include three different types of extreme learning machines,including the standard,online sequential,and kernel extreme learning machines,in addition to the artificial neural network,classification and regression tree model,and statistical multiple linear regression model.The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models.The input variability was assessed based on two scenarios prior to the application of the predictive models.For the first assessment,the machine learning models were developed using all the available cement and concrete mixture input variables;the second assessment was conducted based on the gamma test approach,which is a sensitivity analysis technique.Subsequently,the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches.The adopted methodology attained optimistic and reliable modeling results.The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.
