Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: A comparative study on various machine learning models

The escalating global water crisis necessitates innovative approaches to address freshwater scarcity (Yavari et al. 2022; Nejatian et al. 2023), with a particular focus on sustainable and energy-efficient desalination methods (Kumar et al. 2022). Among the emerging solutions, the air humidification–dehumidification (HD) technique for saltwater desalination, especially in low capacities, has gained prominence (Mohamed et al. 2021; Su et al. 2023). A notable subset of this approach involves solar water desalinations, utilizing solar energy either directly or indirectly (Mutar & Alaiwi 2023). Early attempts primarily employed direct solar water purifiers, and extensive research aimed at enhancing their efficiency (Taner & Dalkilic 2019; Ni et al. 2023). However, issues such as coating clouding, reduced transparency, and diminished device efficacy hindered their widespread commercialization (Duan et al. 2023).

The urgency of addressing global water scarcity, exacerbated by population growth, climate change, and increasing water demands across various sectors, underscores the critical need for innovative solutions (Taner 2015; Ingrao et al. 2023). Desalination, particularly solar-driven methods, presents a promising avenue to augment freshwater supply (Shokri & Sanavi Fard 2023; Zhang et al. 2024). As the world grapples with the challenge of providing clean water to burgeoning populations, it becomes imperative to explore and refine technologies that not only meet these demands but also operate sustainably (Abdelfattah & El-Shamy 2024; Hassan et al. 2024). The intersection of solar energy utilization and desalination techniques offers a potentially transformative solution (Nikolaidis 2023). Therefore, an exploration of the solar desalination system within a net-zero energy consumption building aiming to contribute novel insights into enhancing the efficiency of freshwater production through renewable energy sources is needed.

In contrast, the indirect method harnesses solar energy through technologies like solar collectors, subsequently applying this energy to power the water softening process (Jasim et al. 2023; Saedpanah et al. 2023). This indirect approach boasts higher efficiency overall, as the saltwater avoids direct contact with the solar absorber, mitigating operational challenges (Djellabi et al. 2022). Within the realm of indirect methods, the HD process within solar water tanks has garnered significant attention in recent years. The HD exhibits promising characteristics, offering a viable avenue for freshwater production in an energy-efficient manner (Xue et al. 2023).

Standard mathematical techniques are employed in the optimization and modeling of thermal processes (Li et al. 2023). Theoretically, it is feasible to construct an appropriate model to represent a multitude of thermal processes involved in desalination water (Prado de Nicolás et al. 2023). However, such models cannot be developed for processes characterized by variables and nonlinear relationships. A number of variables influence the distillation process, including the flow rate of the inlet water, the temperature of the environment, the intensity of solar radiation, the velocity of the wind, the concentration level, the length of the concentrator, and the temperature of the absorber plate and glass (Hammoodi et al. 2023). It is exceedingly challenging to formulate a mathematical model that can accurately predict and optimize dependent variables when all independent variables are considered. The incorporation of machine learning models signifies a pioneering approach to assessing and optimizing system performance (Emad Azhar Ali et al. 2021; Chen et al. 2023; Siahaan & Asrol 2023; Wei et al. 2024). This interdisciplinary fusion of renewable energy and advanced computational techniques opens avenues for not only understanding the intricacies of solar-driven desalination but also for innovatively tackling the challenges associated with its implementation (Yaghoubi et al. 2022; Misbah Inayat et al. 2023). The dialogue within this research extends to the nuanced comparison of these models, sparking discussions on their respective strengths, weaknesses, and potential synergies.

To advance this paradigm, the performance of a solar desalination system integrated into a net-zero energy consumption building is developed. This study uses a variety of machine learning models, including the classic artificial neural network (ANN), a hybrid ANN model, and the novel imperialist competitive algorithm and emotional artificial neural network (EANN). These models are used to predict critical parameters such as freshwater production and vapor temperatures using input data that include solar intensity, ambient temperature, inlet water flow, and inlet water temperature. This study makes a significant contribution by conducting a comprehensive comparative analysis of these machine learning models, revealing their efficacy in predicting key performance indicators of the solar desalination system. Its goal is to provide valuable insights into optimizing system efficiency and expanding the applicability of solar water desalination technologies by investigating the strengths and limitations of each model. As a result, this comparative study could add a new perspective to ongoing efforts to address water scarcity through sustainable and energy-efficient means. Meanwhile, due to the limited amount of data required for machine learning models in such scenarios, selecting an appropriate model capable of effectively training and predicting data is crucial.

The solar energy is transferred from the collectors to the water in the tank via a heat exchanger. The control system is configured to activate the solar pump in the event that it detects a potential increase in tank temperature caused by solar collectors, as determined by temperature measurements of the collector's surface and the interior of the tank. d) Flows 5 and 6 in Figure 1, which illustrate the internal fluid circulation as the tank temperature rose to 70 °C, the water desalination commenced operation in response to the control system's instruction. Additionally, the humidifying pump associated with water circulation in the polymer was activated via a control valve. Comparing and contrasting cold water flows within the compressor as well.

The salt water at the outlet of the compressor experiences a temperature increase of up to 60 °C as a result of the heat transfer capability of the compressor and the inherent airflow within the water desalination. The volume of water in the tank is supplemented with preheated water from the compressor; this water is then utilized as sanitary hot water both during the day and at night. A design has been developed to ensure that the quantity of hot water consumed is synchronized with the intensity of the incoming cold water flow, thereby preventing water waste in the device. Similary to the stream number 4, the polymer emits hot water with a temperature ranging from 55 to 60 °C; it enters the hot water tank after exiting the humidification section.

To evaluate the performance of the solar desalination system within a net-zero energy consumption building, a classic ANN model is employed as a predictive tool. The ANN model is chosen for its ability to capture complex nonlinear relationships within the input parameters solar intensity, ambient temperature, inlet water flow, and inlet water temperature and predict the corresponding outputs, namely, the production of fresh water and vapor temperatures.

Before training the ANN model, the input data undergo thorough preprocessing to ensure uniformity and effectiveness (de Oliveira et al. 2023; Ghasemi et al. 2023). This includes normalization of the input features to a standardized scale, preventing dominance by certain variables. The target outputs, representing fresh water production and vapor temperatures, are also scaled accordingly. This preprocessing step is crucial for enhancing the convergence and stability of the neural network during training (Samadi et al. 2021).

To introduce nonlinearity into the model, rectified linear units (ReLU) are chosen as the activation function for the hidden layers. ReLU is well-suited for capturing complex relationships and preventing the vanishing gradient problem. For the output layer, linear activation functions are employed as they facilitate direct regression predictions, aligning with the continuous nature of the output variables (Equation (3)):

ICA is a nature-inspired optimization algorithm that draws inspiration from socio-political concepts. In this algorithm, a population of solutions is categorized into imperialists and colonies, mirroring the hierarchical structure of societies. The main objective is to find optimal solutions through a process of assimilation, where weaker entities strive to improve by aligning with stronger ones, and revolution, introducing randomness to prevent premature convergence (Khalilnejad et al. 2018; Jaafari et al. 2019; Manmohan & Shalij 2022).

The algorithm begins by generating an initial population P of solutions, each represented as a vector in the decision space. A fitness function F(x_i) is then employed to assess the quality of each solution, serving as a guiding metric for the subsequent competitive dynamics. The top M solutions are designated as imperialists, while the rest become colonies.

The algorithm iteratively refines the positions of imperialists and colonies based on assimilation, revolution, and competition outcomes. This dynamic interplay continues until a termination criterion is met, such as a predefined number of iterations or the attainment of a specific fitness threshold (Lei et al. 2019).

To enhance the performance and optimization capabilities of the solar desalination system, an integrated model is proposed, combining the strengths of the ANN and the Imperialist ICA. This hybrid model aims to leverage the learning capabilities of the ANN for predictive modeling while harnessing the competitive dynamics of the ICA for refining the parameters of the solar desalination system (Gao et al. 2020).

The integrated approach begins with the initialization of the ANN model, comprising an input layer representing solar intensity, ambient temperature, inlet water flow, and inlet water temperature. Hidden layers with ReLU activation functions and an output layer with linear activation functions are structured to predict fresh water production and vapor temperatures. The ANN model is trained using the backpropagation algorithm, iteratively updating weights and biases to minimize the error between predicted and actual values.

Simultaneously, the ICA is employed to optimize the parameters of the solar desalination system. The decision variables, analogous to the positions of imperialists and colonies in ICA, are adjusted through the assimilation process. The assimilation probability for each colony toward each imperialist is determined based on the fitness values of the colonies. The position of each assimilating colony is updated, incorporating the influence of the corresponding imperialist. Random perturbations are introduced through the revolution process to maintain diversity and prevent premature convergence. The hybridization of the ANN and ICA involves an iterative process where the ANN's predictions guide the assimilation dynamics of the ICA. The fitness values of the solutions, driven by the ANN's performance, influence the assimilation probabilities and subsequent updates to the decision variables. This collaborative approach aims to capitalize on the learning capacity of the ANN while utilizing the ICA's optimization prowess.

The EANN represents a significant advancement in the field of artificial neural networks (ANN), as it enables neurons to generate agents that possess the ability to modify cognitive, emotional, and executive functions when necessary (Sharghi et al. 2018; Molajou et al. 2021). In further clarification, EANN models represent advancements in the field of ANN models through the incorporation of a synthetic sensing unit that can secrete hormones to regulate the functions of nodes (nuclei). Furthermore, it is possible to dynamically modify the hormone weights in EANN models in accordance with the input and output values of the nodes (Sharghi et al. 2019). One significant benefit of this model is its efficacy in addressing research challenges that involve restricted data accessibility (Nourani et al. 2019b).

The principal objective of the EANN under consideration is to develop an optimal neural network that minimizes computational complexity, thereby enabling the understanding of complex and nonlinear systems.

The effectiveness of the system is substantially impacted by the accuracy of this model. Nevertheless, it is imperative to consider the equilibrium between precision and computational burden. Elevated levels of precision frequently necessitate augmented computational effort and intricacy (Prakash & Srinivasan 2009). Despite the intrinsic nonlinearity of real systems, linear models are frequently utilized to circumvent this.

Throughout the training process, hormonal constants exert influence on other nodal units. In this specific EANN configuration, the output of the ith node, housing the three hormones H_a, H_b, and H_c, is calculated as per the following equations:

The glandularity factor must therefore be calibrated throughout the EANN's training phase. This calibration guarantees that the glands are supplied with an ideal quantity of hormones. It is possible to implement schemes that activate H_h hormone values by employing input samples such as the training dataset's average input values. Following this, hormone values are revised by the learning process in accordance with the output network and the correlations delineated in Equations (4) and (5). The purpose of this iterative procedure is to attain an appropriate correspondence between the goals of the time series and the EANN model.

Here, α represents the learning rate, regulating the step size of the parameter updates. The partial derivative signifies the rate of change of the loss with respect to the weight w, guiding the network to adjust its parameters in the direction that minimizes the error. This iterative process is reiterated until the network converges to a state where the loss is minimized, and the model generalizes well to unseen data.

The outcomes of utilizing ANN with various algorithms and threshold functions (Table 1) indicate that the network with the most accurate prediction of temperature and water vapor production has the 4–16–2 structure. This architecture demonstrates that the middle layer of the network consists of a solitary layer and 16 neurons. As the number of intermediate layers increased, there was little discernible change in the error rate during network training; in fact, in many instances, the error rate even increased. Furthermore, the outcomes of utilizing various activity functions demonstrated that the backpropagation learning algorithm in conjunction with the ReLU activity function could generate accurate predictions.

The results of the machine learning model show that the EANN model has higher accuracy in predicting fresh water production and water vapor temperature compared to traditional ANN and ANN-ICA. The EANN model shows better predictive ability than the ANN and ANN-ICA models for water vapor temperature and fresh water production in solar water desalination plants. The ANN-ICA model shows quicker convergence and modeling execution compared to other models. This characteristic indicates the model's excellence in the specific aspect of modeling time. Based on the results obtained and the features of the EANN model, it can be inferred that these networks have a significant ability to replicate complex and nonlinear processes, such as heat and mass transfer operations in desalination for producing drinkable water. The findings indicate that learning models can accurately depict complex and varied conditions, including ambient temperature, radiation intensity, and other atmospheric factors that affect the functioning of desalination systems. Machine tools are efficient instruments that can replicate these processes inexpensively and quickly. This research's findings align with those presented in the studies carried out by Nazghelichi et al. (2011) and Hamdan et al. (2014).

In light of the concurrent production of two commodities, hot water and potable water, this strategy ought to be evaluated in comparison to alternative approaches. The current and established processes utilized in the manufacturing of these two products require substantial consumption of fossil fuels or electricity equivalents. The application of household membrane water desalinations is restricted to brackish water with total dissolved solids (TDS) greater than 2,000 ppm. This occurs when, according to the test results, the salinity of the incoming water into the solar combination device is not restricted. The quality of the water produced by the desalination is among its most influential functional parameters. The investigation involved the sampling and chemical analysis of both incoming and outgoing water in order to assess the device's performance in water desalination.

Based on the findings, a greater quantity of water salts were captured and observed as a result of the evaporation process and water concentration; the inlet water contained a TDS concentration exceeding 6,000 ppm, whereas the outlet water contained less than 50 ppm TDS. Conversely, to heat 300 L of water to 60 °C using a gas water heater or boiler (with an average efficiency of 80%), approximately 60 MJ of energy is required. In addition to producing 1 L of gasoline, more than 2 m³ of gas are required; 4.2 kg of carbon dioxide are produced when gasoline vehicles are burned, while the combustion of one million cubic feet (28,300 m³) of natural gas results in the emission of 3.60 tons of carbon dioxide. On this basis, wood is required to provide hot water. Using the fuel specified, a residential unit can generate 2.4 kg of carbon dioxide on a daily basis. Approximately 0.56 kg of carbon dioxide is generated as a result of the electricity utilized by this device. The amount will also be reduced to zero if photovoltaic panels are utilized during the day, when it is extremely hot. As a result, the quantity of environmentally detrimental gases emitted by this design is negligible and can be disregarded.

With the increased volume of salt water entering the device being utilized as sanitary hot water, this will result in the conservation of additional water resources in the field of produced wastewater (Ma et al. 2023; Madelatparvar et al. 2023), due to the fact that conventional water desalination apparatuses release the majority of the incoming water as wastewater. With regard to the quantity of saline water released into the environment, it is possible to assert that no saline water enters the environment. A minimal quantity of generated hot water will be discharged locally and in conjunction with the sewage from the building, exclusively on specific days when the amount of sunlight is optimal or when hot water consumption is low. However, depending on the intended use, it may be possible to conserve this water for future use. Consequently, this design exhibits environmental compatibility and has the potential to facilitate the proliferation of renewable energy sources and the construction of structures that consume zero energy.

Examining the solar desalination system integrated into a net-zero energy consumption building, this research endeavor is situated at the critical juncture of solar energy utilization and desalination techniques. By employing a range of machine learning models including classic ANN, hybrid ANN-ICA models, and EANN, the performance of the system can be evaluated and optimized in a novel and innovative manner.

The methodology section provides a comprehensive account of the formulation and execution of the machine learning models. The classic ANN functions as a predictive instrument by identifying intricate connections among input parameters. Concurrently, the ICA implements an optimization strategy inspired by nature, which promotes competitive dynamics in order to enhance system parameters. By integrating ANN and ICA, the integrated model presents an innovative and comprehensive methodology that leverages the learning capabilities of ANN for predictive modeling and the competitive dynamics of ICA for optimization.

In addition, an innovative paradigm is introduced by the EANN, which permits neurons to modify their cognitive, emotional, and executive functions. The research effectively utilizes the EANN model, which is renowned for its resilient performance in hydrological investigations, to tackle the obstacles posed by the scarcity of data.

Model evaluation metrics, including MSE, and R², provide a quantitative assessment of the models' accuracy in predicting crucial parameters. The results indicate that the integrated ANN-ICA model outperforms the classic ANN, displaying superior accuracy and convergence rates. The EANN emerges as the most accurate in predicting freshwater production and vapor temperatures, demonstrating its efficacy in handling studies characterized by limited data availability. In the ANN optimal model, MSE for training, validation, and testing are 0.0011, 0.0013, and 0.0037, with R² of 0.9989, 0.9630, and 0.9862, respectively. In the ANN-ICA optimal model, R² index for the training, testing, and validation phases of this model is 0.9735, 0.9683, and 0.9851, respectively. However, for the EANN model MSE for the training, validation, and test stage is 0.0345, 0.0124, and 0.0347, respectively. Meanwhile, R² for training, validation, and testing phase are 0.9899, 0.9811, and 0.9724, respectively.

The comprehensive comparative analysis extends beyond model evaluation, encompassing environmental assessments. The solar desalination system, concurrently producing hot water and potable water, proves to be environmentally compatible, offering advantages over traditional processes involving substantial energy consumption. The study evaluates the concurrent production of hot water and potable water in comparison to alternative approaches, highlighting the substantial energy consumption in current processes. The investigation focuses on household membrane water desalination, restricted to brackish water with a TDS greater than 2,000 ppm. Chemical analysis reveals a significant reduction in TDS concentration from the inlet water (6,000 ppm) to the outlet water (less than 50 ppm), showcasing the device's desalination performance. Additionally, the design proves environmentally compatible, conserving water resources and minimizing detrimental gas emissions, fostering the proliferation of renewable energy sources and energy-efficient structures. The environmentally friendly nature of the proposed solar desalination system aligns with the growing importance of sustainable and energy-efficient solutions in the face of global water crises.

The authors would like to acknowledge the support of the Deputy for Research and In-novation – Ministry of Education, Kingdom of Saudi Arabia for this research through a grant (NU/IFC/2/SERC/-/14) under the Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.