ArticleScientific reports2026
Development and multi-objective optimization of an automatic filtration system with pressure-based preventive control for efficient irrigation water management.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the limited water resources and the challenge of emitter clogging in irrigation systems, this research aimed to design, construct and optimize an integrated and automated automatic filtration system to overcome the problems of conventional systems with operator dependence and suboptimal water and energy consumption. In this regard, a new system was designed in which a hydrocyclone and a screen filter were integrated into a single chamber and a pressure difference-based control algorithm was implemented to automatically activate the washing operation. The performance of this system was evaluated under the influence of three factors: operating pressure (100, 200, and 300 kPa), suspended solids concentration in water (4, 8, 12%), and screen pore diameter (0.1, 0.2, 0.3 mm) in the form of 81 experiments. The obtained data including filtration efficiency, water and energy consumption, and washing time were analyzed using statistical methods and Random Forest modeling, and a multi-objective optimization process was performed using the NSGA-II algorithm. A multi-objective optimization framework was implemented across three distinct operational scenarios: a quality-oriented scenario prioritizing maximum filtration efficiency; a resource-saving scenario emphasizing the minimization of wash water and energy consumption; and a balanced scenario aiming to achieve a compromise between performance and resource use. The optimization results revealed a set of optimal solutions that clearly illustrate the trade-offs among the considered objectives. The balanced scenario is reported as a representative operational case, achieving filtration efficiencies in the range of 88-98% with wash water consumption of 21-24 L and energy consumption of 80-90 kWh per cycle.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.