Low-cost sensors can provide inaccurate data as temperature and humidity affect sensoraccuracy. Therefore, calibration and data correction are essential to obtain reliable measurements.This article presents a training and testing method used to calibrate a sensor module assembledfrom SO2 and NO2 electrochemical sensors (Alphasense B4 and B43F) alongside air temperature (T)and humidity (RH) sensors.
Field training and testing were conducted in the industrialized coastalarea of Quintero Bay, Chile. The raw responses of the electrochemical (mV) and T-RH sensors weresubjected to multiple linear regression (MLR) using three data segments, based on either voltage(SO2 sensor) or temperature (NO2). The resulting MLR equations were used to estimate the referenceconcentration. In the field test, calibration improved the performance of the sensors after addingT and RH in a linear model.
The most robust models for NO2 were associated with data collectedat T < 10 C (R2 = 0.85), while SO2 robust models (R2 = 0.97) were associated with data segmentscontaining higher voltages. Overall, this training and testing method reduced the bias due to T andHR in the evaluated sensors and could be replicated in similar environments to correct raw data fromlow-cost electrochemical sensors. A calibration method based on training and sensor testing afterrelocation is presented. The results show that the SO2 sensor performed better when modeled fordifferent segments of voltage data, and the NO2 sensor model performed better when calibrated fordifferent temperature data segments.
Sensor Fault Detection Methods Applied On Dissolved Oxygen Sensors At A Full Scale WWTP.
Det finns stor potential i att effektivare övervaka och styra pumpstationer i vatten- och avloppsledningsnät i de allra flesta städer och samhällen. Kan man tidigt upptäcka fel i pumpstationer eller onormala förhållanden i ledningsnätet kan man förbättra reningen samt undvika bräddning med vattenmängder som annars går ut orenade i miljön. Projektets mål är att utveckla metodik och verktyg för övervakning av pumpstationer för att säkerställa att driften är normal och ge tidiga varningar när en pumpstation får problem samt att ta fram en prioritering av var insatser för att minska tillskottsvattnet har störst effekt. Metodiken bygger på att jämföra drifttillståndet från historiska data under normala förhållanden med driftsförhållandet nu och se om man hittar avvikelser. Ett annat sätt att hitta avikelser på är att jämföra pumpstationens driftdata med närliggande pumpar eller pumpstationer för att se om förhållandet förändrats över tid vilket kan bero på slitage eller ökad mängd tillskottsvatten. Projektet har resulterat i konkreta verktyg som kan ha stor nytta vid kommunernas arbete med att förebygga problem i avloppledningsnätet. Vi kan nu göra en automatisk diagnosticering av ett pumpnätverk där användaren får ut vilka stationer som är särskilt drabbade av tillskottsvatten och där mycket kan vinnas på att sätta in åtgärder. Resultaten kan användas för att prioritera de områden där insatser kan göra störst skillnad både ekonomiskt och för miljön. I samband med detta får användaren dessutom ut en enkel uppskattning av hur mycket av det som pumpas som kommer från den faktiska förbrukningen på nätet. Vi har påvisat att drifttiderna i pumpstationerna är korrelerade med regnmängden i området och förhållandet kan anses vara kausalt. Tillsammans med korrelationerna mellan stationerna i avloppsnätet ger det viktig information om tidsförskjutningarna i systemet, vilket i sin tur är viktig för att kunna vidta preventiva åtgärder vid stora regnmängder. Uppföljning på trenden för pumpkvoten, som beräknas genom att ta kvoten mellan pumparna i samma pumpgrop vilka används växelvis, ger en indikation på drift av enskilda pumpar. Metodiken och verktygen kan göra det möjligt för användarna att övergå till behovsstyrt underhåll istället för att arbeta utifrån underhållsscheman. Det kan dessutom möjliggöra ett mer preventivt arbete med färre nödavledningar som följd och minskat antal nödutryckningar. Det krävs dock vidare utveckling för att få ut den fulla potentialen i tekniken. För att ta resultaten vidare behöver samarbetet med Borås Energi och Miljö fortsätta med regelbundna diagnostiseringar. I dagsläget finns det inget enkelt sätt för driftspersonalen att få ut resultaten från verktygen för diagnostisering. Nästa steg blir därför att utveckla ett användargränssnitt för att tillgängliggöra verktygen för användare i Sverige.
Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10–PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9–24%) and PM10 (10–37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47–0.86) than PM10 (0.24–0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63–0.87 for continuous monitoring and 0.69–0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.
S´wash is an idea grant support project sponsored by MISTRA to create innovative research projects with great potential to improve the environment. S´wash has decreased the water usage down to a consumption of 10.3 litres of water for a washing load of three kilograms of textiles, a decrease of 79 percent compared to the standard washing machine used in European households today. During the course of the project a lot of different techniques and approaches have been tested to find suitable solution to implement in a washing machine. Two prototypes have been built to evaluate solutions and ideas. The prototypes are based on standard washing machines but the solutions are not yet ready for production.
A decision support system (DSS) for control of the coagulant dosage at the Görväln drinking water plant has been developed and implemented. The goal with the DSS was to enable the transition from manual to automatic control of coagulant dosage. The DSS is based on a multivariate statistical regression (PLS) model mimicking the operators’ manual dosage of coagulant and is based solely on UV-absorbance, colour, COD, TOC and conductivity in the raw water. By external validation with two years of historical data, the model was proven to provide a good estimation of the manual dosage. When the model was implemented for dosage control, the variation of the quality of the treated drinking water was significantly reduced as a result of quicker and correct response to changes in the raw water and at the same time the coagulant consumption was maintained. The results pave the way for future optimization of the coagulant dose, resulting in reduced coagulant consumption while still maintaining or even increasing the drinking water quality. The approach presented is expected to be able to give positive results on other drinking water plants as well.
IVL, together with Emerson Process Management, has developed a decision support system (DSS) based on multivariate statistical process models. The system was implemented at Nynas AB's refinery in order to provide real-time TBP curves and to enable the operator to optimise the process with regards to product quality and energy consumption. The project resulted in the following proven benefits at the industrial reference site, Nynas Refinery in Gothenburg: o Increased yield with up to 14 % (relative terms) for the most valuable product o Decreased energy consumption of 8 % Validation of model predictions compared to the laboratory analysis showed that the prediction error lay within 1°C throughout the whole test period.
During 2010 and 2011, IVL has in a case study together with Geodis Wilson applied the so-called Sensitivity Model Prof. Vester (a system model for which IVL has acquired a license and which is characterized by a very broad approach) in the analysis of a transport flow (clothes) from Asia to Europe. The case study aims to identify relevant control parameters, which help the transport provider and the producer of clothes to optimize and adapt their transport flows according to changed conditions (economic, ecological, social) today and tomorrow.