A process is provided for the determination of oil dispersant effectiveness. A submersible dispersant sensing platform is passed across a body of water. The platform has a plurality of sensors including a multichannel fluorometer and a particle size analyser, and each sensor produces an output data stream. The body of water is continuously analysed at a predetermined depth profile below the surface of the body of water. Hydrodynamic and environmental condition data is collected proximate in time and location to the output data from the dispersant sensing platform. The environmental condition data includes one or more of ambient temperature, body or water temperature, salinity of the body of water, wind speed, location, mixing energy of the body of water and derivatives thereof. Oil and dispersant data is provided which includes characteristics of the dispersant and of oil samples prior to the application of the dispersant. The output data stream, the hydrodynamic and environmental condition data, and the oil and dispersant data is processed to generate an indicator of the state of dispersion of the oil and of the oil dispersant efficiency under the hydrodynamic and environmental conditions the oil is exposed to. A system for the determination of oil dispersant efficacy is also provided.

Methods, systems, and apparatuses are provided for detecting and determining conditions of and conditions within a fluid conduit.

Methods, systems, and apparatuses are provided for detecting and determining conditions of and conditions within a fluid conduit.

An embodiment provides a method for measuring nitrate in an aqueous sample, including: introducing an aqueous sample containing an amount of nitrate to a cation exchange resin; flowing the aqueous sample over copper metal; adding a reducing reagent to the aqueous sample; adding a colorimetric indicator to the aqueous sample; and measuring the amount of nitrate in the aqueous sample by measuring a change in intensity of the absorbance. Other aspects are described and claimed.

Methods for determining the toxicity of fresh-water and marine sediments and sediment pore water containing indigenous or introduced toxicants from each as a one-time analysis and/or for analysis over a period of time are provided. The present disclosure further provides kits assembled for the afore-mentioned determination. The methods and kits can be used for analyzing sediment and pore water samples from, among other locations, all environments where species having a dormant life stage may exist including, for example, natural zooplankton.

A method for determining a water treatment plan for produced water includes receiving sample water analysis for the produced water, and receiving one or more key performance indicators (KPIs) that each indicate a selected treatment result for the produced water. In addition, the method includes providing the sample water analysis and the KPIs to a machine learning model and determining a water treatment plan for the produced water using the machine learning model, wherein the water treatment plan comprises one or more additives for the produced water that are to provide the produced water with the KPIs.

A method for determining a water treatment plan for produced water includes receiving sample water analysis for the produced water, and receiving one or more key performance indicators (KPIs) that each indicate a selected treatment result for the produced water. In addition, the method includes providing the sample water analysis and the KPIs to a machine learning model and determining a water treatment plan for the produced water using the machine learning model, wherein the water treatment plan comprises one or more additives for the produced water that are to provide the produced water with the KPIs.

A control method based on an adaptive neural network model for dissolved oxygen of an aeration system includes: obtaining related water quality monitoring data of a sewage treatment plant, and performing data preprocessing on the related water quality monitoring data; performing principal component analysis on the preprocessed related water quality monitoring data and a dissolved oxygen concentration of the aeration system through a principal component analysis method, and determining a water quality parameter with a highest rate of contribution to a principal component; taking the water quality parameter with the highest rate of contribution to the principal component, and predicting a dissolved oxygen concentration of the aeration system; and optimizing a dissolved oxygen predictive value obtained by means of the adaptive neural network model to obtain an optimal regulation value, and performing online regulation on a fuzzy control system of the adaptive neural network model.

A method for the detection of hydrocarbon contamination in a sample is disclosed. The method includes contacting a sample with a molecular probe. The molecular probe has a photoluminescence which is environmentally sensitive. The photoluminescence from the molecular probe is collected. The method includes determining whether the photoluminescence is indicative of a hydrocarbon contaminated sample. A test strip for the detection of hydrocarbon contamination in a sample is also disclosed. The test strip includes a molecular probe embedded in a substrate and/or immobilized to the substrate, the molecular probe having a photoluminescence which is environmentally sensitive to hydrocarbon contaminated sample.

A novel underwater robot water quality data acquisition device includes a casing, a thruster group, an upper cabin, a lower cabin, a buoy cabin, an upper cabin tray, a lower cabin tray, a power supply assembly, a power conditioning module, a data acquisition control module, a water quality sensor assembly, and a wireless Internet of Things (IoT) module. The device can convert the power supply voltage required by each other module through the power management module. The data acquisition control module transmits signals to the water quality sensor assembly in a set timing sequence, performs real-time reading and processing of water quality data fed back from the sensor, and uploads the processed water quality data to the data platform through the wireless IoT module, thereby achieving the display and preservation of water quality data.