Abstract

The use of renewable energies for pumping and desalination of seawater and/or brackish water can be a viable solution to reach a more sustainable freshwater production and reduce environmental impacts. In this way, a new approach based on the design of experiment method for optimal sizing a stand-alone solar–wind-reverse osmosis desalination system is investigated. For this, system modelling is presented with the development of a single sizing parameter between desalination motor pump and reverse osmosis unit and a dynamic simulator of the proposed energy–water system with its energy management loop is developed using climatology year data of southern Tunisia. In optimization loop, the methodology with one-year dynamic simulation necessarily leads to very long convergence times of several days. To try to reduce this time, a track has been proposed which consists in using meta-models instead of dynamic simulators. In order to apply this track, a meta-model (hybrid spline) that represents the system constraints and objectives is investigated based on design of experiments tool and a bi-objective genetic algorithm is applied in the optimization process via the developed meta-model. Two objective functions are used: loss of power supply probability (reliability indicator) which is used to present the dissatisfaction of the water production and embodied energy (environmental indicator) which is used to compute energetic cost (MJ) and to evaluate the environmental impacts potential (across the whole life cycle). Optimal sizing of the system via meta-models instead of dynamic simulator led to encouraging results with significantly reduced CPU times (from several days to 13 min).

Graphic abstract

中文翻译：

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混合可再生能源系统驱动的海水淡化装置的智能优化

摘要

将可再生能源用于海水和/或微咸水的泵送和淡化可能是实现更可持续的淡水生产并减少环境影响的可行解决方案。通过这种方式，研究了一种基于实验方法设计的新方法，该方法可优化独立式太阳-风-反渗透淡化系统的尺寸。为此，通过在海水淡化泵和反渗透单元之间建立一个单一的尺寸参数，提出了系统建模，并使用突尼斯南部的气候年数据开发了所提出的能量-水系统的动态模拟器及其能量管理回路。在优化循环中，具有一年动态仿真的方法必然导致几天的非常长的收敛时间。为了减少这段时间，已经提出了一种轨道，该轨道包括使用元模型而不是动态模拟器。为了应用此轨迹，基于实验工具的设计，研究了代表系统约束和目标的元模型（混合样条），并通过开发的元模型在优化过程中应用了双目标遗传算法。使用了两个目标函数：供电概率的损失（可靠性指标），用于表示水生产的不满意程度；体现的能源（环境指标），用于计算能源成本（MJ）并评估潜在的环境影响（在整个生命周期中）。

图形摘要