A method of controlling cooling water treatment may involve measuring operating data of one or more downstream heat exchangers that receive cooling water from the cooling tower. For example, the inlet and outlet temperatures of both the hot and cold streams of a downstream heat exchanger may be measured. Data from the streams passing through the heat exchanger may be used to determine a heat transfer efficiency for the heat exchanger. The heat transfer efficiency can be trended over a period of time and changes in the trend detected to identify cooling water fouling issues. Multiple potential causes of the perceived fouling issues can be evaluated to determine a predicted cause. A chemical additive selected to reduce, eliminate, or otherwise control the cooling water fouling can be controlled based on the predicted cause of the fouling.

The present invention relates to a method of monitoring and optionally controlling removal of microplastics from microplastic containing raw water, drinking water, storm water, water originating from melted snow, surface water, effluent of industrial wastewater treatment plants, effluent of municipal wastewater treatment plants, industrial process water, using at least one coagulant and/or polymer, wherein the number of microplastic particles of the microplastic containing water before and/or after addition of said at least one coagulant and/or polymer is determined by using an optical measurement measuring light scattering and fluorescence of particles in a predetermined volume of the microplastic containing water.

Methods and systems for water quality assessment are disclosed. The method includes obtaining a first input data indicative of properties of a first liquid sample, the first input data including turbidity data and total suspended solids data for the first liquid sample, where the first liquid sample is acquired from a liquid source. The method further includes determining, using a computer processor and a machine learning model, a first predicted particle-size distribution of the first liquid sample based on the first input data, where particle-size distribution is controlled, at least in part, by a set of dosage parameters configurable by a water quality system. The method further includes determining, with an optimizer applied to the machine learning model, an optimal set of dosage parameters based on the first predicted particle-size distribution and adjusting the set of dosage parameters of the water quality system to the optimal set of dosage parameters.