The need for better microplastic removal from wastewater streams is clear, to prevent potential harm the microplastic may cause to the marine life. This paper aims to investigate the efficacy of electrocoagulation (EC), a well-known and established process, in the unexplored context of microplastic removal from wastewater streams. This premise was investigated using artificial wastewater containing polyethylene microbeads of different concentrations. The wastewater was then tested in a 1 L stirred-tank batch reactor. The effects of the wastewater characteristics (initial pH, NaCl concentration, and current density) on removal efficiency were studied. Microbead removal efficiencies in excess of 90% were observed in all experiments, thus suggesting that EC is an effective method of removing microplastic contaminants from wastewater streams. Electrocoagulation was found to be effective with removal efficiencies in excess of 90%, over pH values ranging from 3 to 10. The optimum removal efficiency of 99.24% was found at a pH of 7.5. An economic evaluation of the reactor operating costs revealed that the optimum NaCl concentration in the reactor is between 0 and 2 g/L, mainly due to the reduced energy requirements linked to higher water conductivity. In regard to the current density, the specific mass removal rate (kg/kWh) was the highest for the lowest tested current density of 11 A/m2, indicating that low current density is more energy efficient for microbead removal.
Background and Purpose Motor failure in multi-leaf collimators (MLC) is a common reason for unscheduled accelerator maintenance, disrupting the workflow of a radiotherapy treatment centre. Predicting MLC replacement needs ahead of time would allow for proactive maintenance scheduling, reducing the impact MLC replacement has on treatment workflow. We propose a multivariate approach to analysis of trajectory log data, which can be used to predict upcoming MLC replacement needs. Materials and Methods Trajectory log files from two accelerators, spanning six and seven months respectively, have been collected and analysed. The average error in each of the parameters for each log file was calculated and used for further analysis. A performance index (PI) was generated by applying moving window principal component analysis to the prepared data. Drops in the PI were thought to indicate an upcoming MLC replacement requirement; therefore, PI was tracked with exponentially weighted moving average (EWMA) control charts complete with a lower control limit. Results The best compromise of fault detection and minimising false alarm rate was achieved using a weighting parameter (λ) of 0.05 and a control limit based on three standard deviations and an 80 data point window. The approach identified eight out of thirteen logged MLC replacements, one to three working days in advance whilst, on average, raising a false alarm, on average, 1.1 times a month. Conclusions This approach to analysing trajectory log data has been shown to enable prediction of certain upcoming MLC failures, albeit at a cost of false alarms.