To further determine the fouling behavior of bovine serum albumin (BSA) on different hydrophilic PVDF ultrafiltration (UF) membranes over a range of pH values, self-made atomic force microscopy (AFM) colloidal probes were used to detect the adhesion forces of membrane–BSA and BSA–BSA, respectively. Results showed that the membrane–BSA adhesion interaction was stronger than the BSA–BSA adhesion interaction, and the adhesion force between BSA–BSA-fouled PVDF/PVA membranes was similar to that between BSA–BSA-fouled PVDF/PVP membranes, which indicated that the fouling was mainly caused by the adhesion interaction between membrane and BSA. At the same pH condition, the PVDF/PVA membrane–BSA adhesion force was smaller than that of PVDF/ PVP membrane–BSA, which illustrated that the more hydrophilic the membrane was, the better antifouling ability it had. The extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) theory predicts that the polar or Lewis acid–base (AB) interaction played a dominant role in the interfacial free energy of membrane–BSA and BSA–BSA that can be affected by pH. For the same membrane, the pH values of a BSA solution can have a significant impact on the process of membrane fouling by changing the AB component of free energy.
• UV-vis absorption analyzer was applied in drainage type online recognition.• The UV-vis spectrum of four drainage types were collected and evaluated.• A convolutional neural network with multiple derivative inputs was established.• Effects of different network structures and input contents were compared. Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%. 相似文献
