The invention relates to a method for cleaning a sensor in a wastewater monitoring arrangement comprising at least one sensor with a sensor surface, such as an optical sensor with a window, lens, or the like. In the monitoring arrangement, during a normal operation mode, a sample flow of wastewater is arranged to flow past the sensor surface and the sensor is arranged to provide measurement values that describe a quality parameter of the wastewater. The method comprising steps of: starting a sensor cleaning cycle by discontinuing the sample flow; starting a cleaning liquid flow; arranging the cleaning liquid flow towards the sensor surface; mechanically cleaning the sensor surface by an automatic cleaning device; discontinuing the cleaning liquid flow after a predetermined cleaning time and ending the cleaning cycle; and starting the wastewater flow again.

Method and systems for measuring growth rate in plant or aquatic animal species such as embryonic or adult fish. The methods and systems utilize the measurement of NADH.sub.2 production by detecting a colorimetric and fluorescent shift when a redox indicator such as resazurin is added to a sample. The colorimetric/fluorescent shift is indicative of the reduction of the redox indicator by NADH.sub.2. The methods and systems of the present invention may be used to predict growth potential of a plant or animal, and measuring the growth rate of said plant or animal may be helpful for identifying and selecting individuals within a group that have greater growth potential. The methods and systems of the present invention may help eliminate the need for special equipment (e.g., for measuring oxygen consumption), decrease variability of measures, and minimize the effects of external factors (feeding/hormonal status).

Measurement arrangement for determining a constituent substance or quality parameter of water by thermal decomposition in a reaction module, delivery of a reaction product to a detector, and evaluation of a detector signal for deriving a value of the constituent substance or quality parameter, wherein the reaction module is an elongated vessel having internal heating, and has a head section into which the sample is introduced, a reaction zone, as well as a foot section, from which the reaction product is output, wherein the reaction module, the heating, and means for the supply of samples and carrier gas are configured such that during the operation of the measurement arrangement an outer head temperature is T.sub.H≤80° C., and an outer foot temperature is T.sub.F≤150° C. at a maximum temperature in the reaction zone of T.sub.MAX≥1150° C.

There is provided a biofilm capacitance microbial electrochemical cell (MEC) sensor to measure organic carbon in water and wastewater rapidly and accurately, represented by the 5-day biochemical oxygen demand (BOD.sub.5). The MEC runs at charging (open circuit) and discharging (close circuit) conditions alternately to improve the sensitivity, response time and accuracy. The detectable BOD.sub.5 concentrations with the biofilm-capacitance MEC range from 5 to 250 mg/L (R.sup.2>0.9). The MEC sensor enables BOD.sub.5 measurements at every 2 minutes (1 minute charging and 1 minute discharging), indicating semi-continuous quantification of organic carbon in water and wastewater.

Devices and methods are disclosed for determination of conductivity without inorganic carbon contribution in aqueous process streams. In particular, devices and methods for determining the ionic conductivity of aqueous process streams containing dissolved CO.sub.2.

The present disclosure provides a color chart and a test kit for estimating chemical oxygen demand of water. The color chart includes a blue component, an indigo component, an umber component, and an orange component. The test kit includes an oxidant, a reductant, an indicator, and the color chart. The present disclosure also provides a method for estimating chemical oxygen demand of water. The method includes providing a water sample; adding an oxidant to the water sample; heating the water sample; adding a reductant to the water sample; adding an indicator to the water sample, such that the water sample develops a color; and matching the color of the water sample with the color components of the color chart to estimate the chemical oxygen demand of the water sample.

A method and system are disclosed for determining surfactant concentration levels in aqueous solutions. In accordance with the method of the present disclosure, a related carbon parameter, such as COD or TOC, is measured in an aqueous solution. This measurement is then converted to surfactant concentration using a mathematical correlation or reference data. Through the method and system of the present disclosure, surfactant concentrations in process streams can be monitored and adjusted on the fly.

A processor-based method of determining a characteristic of a fluid, the method comprising: obtaining spectral emission signature (SES) data of the fluid, wherein the SES data comprises, for one or more SES channels: intensity of radiation emitted by the fluid, in one or more channel emission frequency bands, at least partially in response to excitation of molecules of the fluid by received radiation of a respective channel transmitted frequency; and utilizing a machine learning model to determine, from the obtained SES data, data indicative of one or more characteristics of the fluid, wherein the machine learning model was trained in accordance with, at least, a plurality of training examples, one or more of the training examples comprising: SES data of a fluid sample, and one or more fluid characteristics of the fluid sample.

A chemical oxygen demand (COD) of a sample including water is determined. At least two test specimens are obtained. Each of the test specimens include a mixture of the sample and a standard solution including potassium hydrogen phthalate (KHP) in a known concentration. Each of the specimens include a same amount of the sample. Each of the specimens include different amounts of the standard solution. Each of the specimens is diluted with water. A COD of each of the specimens is measured. A COD of the sample is determined based on the measured CODs of the specimens.

A total nitrogen intelligent detection system based on multi-objective optimized fuzzy neural network belongs to both the field of environment engineer and control engineer. The total nitrogen in wastewater treatment process is an important index to measure the quality of effluent. However, it is extremely difficult to detect the total nitrogen concentration due to the long detection time and the low prediction accuracy in the wastewater treatment process. To solve the problem, multi-objective optimized fuzzy neural network with global optimization capability may be established to optimize the structure and parameters to solve the problem of the poor generalization ability of fuzzy neural network. The experimental results show that total nitrogen intelligent detection system can automatically collect the variables information of wastewater treatment process and predict total nitrogen concentration. Meanwhile, in this system, the detection method can improve the prediction accuracy, as well as ensure the total nitrogen concentration be obtained in real-time and low-cost.