A model for the design of optimal total water network (OTWN)

• Main Authors: Sehnaz, S., Lim, J. S., Wan Alwi, S. R., Manan, Z. A.
• Format: Conference or Workshop Item
• Published: 2015
• Subjects: TP Chemical technology
• Online Access: http://eprints.utm.my/id/eprint/62220/; http://www.dubrovnik2015.sdewes.org/

Water reuse network can tremendously reduce the freshwater consumption and wastewater generation. However, it requires major investment in the piping and pumping as well as reuse tank. Many industries still prefer to buy freshwater and treating all their wastewater as it is typically cheaper. However, this scenario is changing as the freshwater price, wastewater penalty to achieve the discharge standard, wastewater treatment cost and land price are increasing. There are many works in minimising water and wastewater cost in the industries. However, most of the work simplifies the wastewater treatment cost into simple equations that favours water reuse. The aim of this paper is to develop a cost effective MINLP model for design of optimal water reuse and wastewater technology treatment system, which is also known as Optimal Total Water Network (OTWN). This model may be useful in the planning of new facilities and introduce the concept of water reuse and wastewater treatment plant optimisation to designers and decision maker. The wastewater treatment technology selection is typically done via an engineer’s experiences. This paper proposed a new approach based on mathematical modelling method considering all the trade-offs and considering a more detail calculations of the wastewater treatment plant. The wastewater treatment is assumed to consist of primary and secondary treatment units with different technology and cost functions. The model is applied to a cardboard mill production in a pulp and paper industry and the result has shown that the model is able to determine the optimal treatment technology while providing cost comparison and accounting for all the economic parameters such as land, piping and power consumption costs.