Allocation of energy in surface water treatment plants for maximum energy conservation

Surface water treatment plants are major energy consumers in all countries. In recent years, the increasing scarcity of fossil fuels and the growth in demand for energy resulting from the needs of development have prompted optimisation of the use of energy. SWTPs are responsible for the supply of treated water to consumers. Although a significant amount of the total energy produced is consumed by WTPs, the utilisation of this resource is variable, and sufficient amounts of it remain unutilised or wasted in the treatment process. Energy resources supplied to a WTP must be optimally allocated. At present, no mechanism exists to ensure this, and allocation is performed as and when it is needed, with no regulation or control. As a result, much energy is returned unutilised. This results in excess expenditures and affects carbon emissions from the plant because both too much and too little utilisation of energy in running water treatment equipment can result in the generation of greenhouse gases to the atmosphere. Unnecessary consumption of energy reduces its availability for other users. Thus, the economy, the environment, and social well-being are affected by the non-optimal utilisation of energy. This problem is common to all parts of the world but is especially acute in developing countries. Lack of intelligent allocation methods compromises the sustainability not only of the plant but also of the dependent population. Here, nature-based optimisation algorithms (OAs) and a modified analytical hierarchy process (mAHP), an objective multi-criteria decision-making method, were utilised to conduct intelligent, automatic allocation of energy among elements of wastewater treatment plants (WTPs). OAs are used to weight elements according to their relative capacity to ensure reliability and restrict risk to plants (resulting in a reliability–risk index); energy is allocated accordingly using mAHP. Tested at a working WTP in India, it minimised energy wastage down to 0.037% of total energy. This is the first attempt to combine mAHP and aggregated output from two OAs to optimise energy use in a WTP (based on the novel reliability–risk index). Our method builds on the concepts of multi-criteria decision making and metaheuristics optimisation algorithms to develop a new procedure for cognitive allocation of energy ensuring optimal performance while minimising the use of energy. Our decision support system can help maximise productivity, and safeguard sustainability, of