Investigation of dehumidifier performance parameters using ANN-PSO algorithm

Varde, Devkant; Arya, Manoj

doi:10.1590/1517-7076-RMAT-2025-0022

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Articles • Matéria (Rio J.) 30 • 2025 • https://doi.org/10.1590/1517-7076-RMAT-2025-0022 linkcopy

Investigation of dehumidifier performance parameters using ANN-PSO algorithm

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

In this investigation, a performance analysis of a liquid desiccant-based dehumidification system was conducted by integrating a particle swarm optimization (PSO) algorithm and an artificial neural network. Experimental data are collected through past studies on falling film towers for flat plate and cylindrical surfaces, covering a wide range of liquid desiccant and air operating conditions. The neural network is fine-tuned through the use of the PSO algorithm. This optimization aims to enhance the accuracy of predicting the moisture absorption rate, change of specific humidity and dehumidification effectiveness of a liquid LiCl (Lithium chlorite) desiccant system. The effectiveness is contingent on various working parameters, including mass flow rate of moist air, mass flow rate of liquid LiCl solution desiccant, inlet air temperature, and relative humidity of inlet air, inlet temperature LiCl solution desiccant and desiccant concentration. The present ANN-PSO algorithm predicts the changes of absolute specific humidity, moisture absorption rate of water vapour from air and effectiveness of the dehumidification system. The present model precisely predict the performance parameters with R² = 0.9989.

Keywords:
Liquid desiccant; Dehumidifier; Polypropylene; Artificial Neural Network (ANN); Particle Swarm Optimization (PSO)

AI TECHNIQUE	R² (TYPICAL RANGE)	AVG. ERROR (%)	INTERPRETABILITY	SUITABILITY FOR NONLINEAR SYSTEMS	REMARKS
SVM	0.85–0.92	5–7	Low	Moderate	Sensitive to parameter tuning; may underfit complex systems
ELM	0.88–0.94	3–6	Moderate	Moderate	Very fast training but lacks iterative optimization
ANFIS	0.90–0.96	2.5–5	High	Moderate	Good for small datasets; rule explosion limits scalability
ANN only	0.95–0.98	1.5–3	Low	High	Requires careful architecture tuning
ANN-PSO (Present Study)	0.996–0.999	<1%	Low	Very High	Hybrid model offers superior accuracy and convergence control

TECHNIQUE	APPLICATION	FINDINGS	DETAILS	REF.
PSO	Hydronic Design Optimization	Recommended particles: 8 for 2 floors, 32 for 28 floors	Applied to closed-loop systems from 2 to 28 floors.	EBERHART and KENNEDY [²⁷]
PSO	Energy Consumption Prediction	Reduces energy waste	Predicts energy consumption based on ambient temperature and occupancy.	BRAVO and FLOCKER [²⁸]
ANN	HVAC System Selection for Public Transport	High result validity	Increases efficiency and reduces selection time.	BEDI et al. [²⁹]
ANN	Chilled Water Pump Troubleshooting	Errors within acceptable limits	Multiple hidden layer architecture used.	BEINARTS [³⁰]
PSO	PID Controller Tuning for Temperature Control	Achieves optimal control response and energy savings	Optimizes PID parameters for HVAC systems.	ADHIKARY et al. [³¹], JUN and KANYU [³²]
ANN	Energy and Exergy Analysis of RACHP Systems	Acceptable accuracy	Applied to various HVAC components and systems.	ZHANG and ZHANG [³³]
ANN	Automotive HVAC System Control	Performance constrained by training data	Enhances energy efficiency and thermal comfort.	MOHANRAJ et al. [³⁴]
PSO	Central Air Conditioning System Optimization	Provides trade-offs between comfort and energy consumption	Multi-objective optimization for energy consumption and comfort.	MENDES et al. [³⁵]
PSO	Refrigeration System Optimization	Increases coefficient of performance by 7.98%	Optimizes operation of refrigerant compressors.	YAO and JAITEH [³⁶]
IPSO	Central Air Conditioning Water System Optimization	Energy saving rate over 18%	Improves energy efficiency.	CHEN et al. [³⁷]
ANN	Cooling Load Forecasting	Relative error below 5%	Predicts cooling loads for HVAC systems.	LI and WANG [³⁸]
ANN	Building Energy Analysis	High accuracy with new ANN models	Predicts energy consumption and indoor air temperature.	SHOLAHUDIN et al. [³⁹]
ANN	Heating and Cooling Load Prediction	Encouraging results with low validation error	Uses 8 input attributes for prediction.	MOHANDES et al. [⁴⁰]
ANN	Filter Differential Pressure Prediction	Improved performance with decision tree approach	Predicts maintenance requirements.	NWULU [⁴¹]
ANN	Automotive Air Conditioning System Performance	Correlation coefficients 0.968–0.999, mean relative errors 1.52–2.51%	Predicts performance parameters.	TEHRANI et al. [⁴²]
ANN	Variable Refrigerant Flow System Performance	Relative error lower than 10%	Predicts cooling capacity.	HOSOZ and ERTUNC [⁴³]
ANN	Optimal HVAC Load Scheduling	Effective in demand response	Online supervised learning for commercial buildings.	SHOLAHUDIN [⁴⁴]

ANN ARCHITECTURE R² VALUES IN THE TRAINING, VALIDATION, AVERAGE VALUE TESTING
ARCHITECTURE	TRAINING	VALIDATION	TESTING	TOTAL
Change of specific humidity (g/kg of dry air)
5-2-1	0.71	0.79	0.78	0.76
5-5-1	0.73	0.73	0.81	0.76
5-10-1	0.73	0.81	0.77	0.77
5-11-1	0.76	0.88	0.72	0.79
5-2-2-1	0.75	0.81	0.8	0.79
5-5-5-1	0.77	0.81	0.79.	0.79
5-10-10-1	0.79	0.75	0.73	0.76
5-11-11-1	0.8	0.77	0.78	0.78
Moisture removable rate (g/sec)
5-2-1	0.97	0.98	0.95	0.97
5-5-1	0.96	0.96	0.98	0.97
5-10-1	0.98	0.98	0.95	0.97
5-11-1	0.98	0.98	0.95	0.97
5-2-2-1	0.97	0.98	0.97	0.97
5-5-5-1	0.98	0.98	0.97	0.98
5-10-10-1	0.94	0.91	0.92	0.92
5-11-11-1	0.97	0.90	0.97	0.95
Humidity effectiveness (%)
5-2-1	0.71	0.79	0.8	0.77
5-5-1	0.76	0.78	0.81	0.78
5-10-1	0.84	0.85	0.77	0.82
5-11-1	0.77	0.88	0.72	0.79
5-2-2-1	0.78	0.92	0.91	0.87
5-5-5-1	0.84	0.89	0.91	0.88
5-10-10-1	0.79	0.75	0.73	0.76
5-11-11-1	0.91	0.81	0.79	0.84

FOLD	R² (SPECIFIC HUMIDITY Δω)	R² (MOISTURE RATE m_aβs)	R² (EFFECTIVENESS ε)	AVG. % ERROR
1	0.9962	0.9948	0.9937	0.89%
2	0.9971	0.9959	0.9945	0.76%
3	0.9967	0.9963	0.9951	0.74%
4	0.9958	0.9942	0.9939	0.92%
5	0.9969	0.9955	0.9947	0.81%
Average	0.9965	0.9953	0.9944	0.82%

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