Simultaneous Optimisation of Mass and Property-Based Resource Conservation Network
Owing to the tonnes of pollutants that are being disposed to the environment, environmental protection has become a major concern around the world. In order to control and diminish pollutants that are discharged into the environment, stringent environmental regulations are enforced. Process industries have been seriously affected from these restrictions due to their large amount of pollutants. The goal of conserving natural resources and reducing industrial pollution is one of the motivations that encouraged engineers to find new techniques to optimise the process performance and to reduce operational costs by minimising fresh resources consumption and waste production. One of the active areas for cost reduction activities has been that of resource conservation via material reuse/recycle, with the most active area being mass integration (El-Halwagi and Manousiouthakis, 1989, Wang and Smith, 1994; El- Halwagi, 1997, 2006; Alves and Towler, 2002; El-Halwagi et al. 2003; Manan et al. 2004; Prakash and Shenoy, 2005; Foo and Manan, 2006). However, many industrial applications are not directly governed by chemical nature of the stream, but are driven by its properties or functionalities (Shelley and El-Halwagi, 2000; El-Halwagi et al.,2004, Grooms et al., 2005, Foo et al., 2006b). To date, most techniques have been developed to handle process constrains which is either composition-based (via mass integration) or property-based. No work has been reported to handle the synthesis of resource conservation network that is governed by both constraints. This is the objective of this work. A mathematical-based optimisation technique is developed, aiming to minimise total annualized cost for the resource conservation network that is governed by both mass and property constraints. A mixed-integer linear programming (MINLP) formulation is solved for a case study of phenol production from cumene hydroperoxyde to illustrate the new problem and devised solution algorithm.
Mass integration Optimisation Property integration Process integration.
Ana Carolina HORTUA Denny Kok Sum NG Mahmoud EL-HALWAGI Dominic Chwan Yee FOO
Department of Chemical Engineering,Texas A&M University,College Station,TX 77843,USA. School of Chemical and Environmental Engineering University of Nottingham Malaysia,Broga Road,43500
国际会议
西安
英文
2007-08-15（万方平台首次上网日期，不代表论文的发表时间）