Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.

The production process of ultra-pure water (UPW) involves dozens of unit processes such as reverse osmosis (RO), pretreatments, membrane degasifier, and several ion exchange processes. Recently, continuous electrodeionization (CEDI) has replaced the 2-bed and 3-tower (2B3T) ion exchange process. As a result, the majority of wastewater in UPW production now comes from the RO concentrate. The important of RO in UPW production is to produce high-quality water with a low ion concentration (around 1 mg/L) for CEDI feed water. Minimizing RO concentrate is essential to reduce the wastewater produced in the UPW production process. This can be achieved by maximizing the recovery of the RO system. However, increasing the recovery is limited by the water quality of the RO permeate. To ensure high-quality permeate water, the RO system is designed with a two-pass configuration. The recovery of each pass in the RO system is limited (e.g., < 85%) due to the expected increase in permeate water concentration at higher RO feed water concentrations. Interestingly, tests using 4-inch RO modules with low concentration feed water (≤ 35 mg/L as NaCl) revealed that the permeate concentration remains almost constant regardless of the feed water concentration. This implies that the recovery of the first RO pass can be increased as long as the average feed/concentrate concentration of the second RO pass is less than 35 mg/L. According to this design criterion for the RO system, the recovery of the first and second RO pass, with a feed water concentration of 250 mg/L as NaCl, can be increased up to 94.8% and 96.0%, respectively. Compared to the conventional RO system design (e.g., 70% and 80% for the first and second RO pass) for UPW production, this maximum recovery design reduces the volume of RO feed and concentrate by up to 38.4% and 89.2%, respectively.

In real world applications, the response of structures may be dependent on the rate of loading and thus can be affected by transient loading, especially when the rate of loading is significant. In such situations, the rate of loading may become a major issue to understand structures during earthquake excitation or under blast or high velocity impact. In some cases, the rate effect on structures under strong earthquake excitation cannot be ignored when attempting to understand inelastic behavior of structures. M any researchers developed the constitutive theories in cyclic plasticity and viscoplasticity. In this study, numerical simulation by cyclic visocoplasticity models is introduced and analyzed. Finally, the analytical results are compared with experimental results as a means to evaluate and verify the model.