CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
An approximate mixed integer linear programming (MILP) model for the design of water reuse/recycle networks with minimum emergy
Authors
C. L. Chiang
D. C.Y. Foo
+4 more
S. Hul
V. Ku-Pineda
D. K.S. Ng
R. R. Tan
Publication date
1 November 2007
Publisher
Animo Repository
Abstract
Recent trends in process engineering have placed increased emphasis on the design of inherently clean and efficient processes. For example, a wide range of pinch analysis and mathematical programming methods have been developed for designing schemes for water reuse/recycle in industrial plants for both grassroot design and plant retrofit. In the latter case, the conventional approach is to maximize water recovery and thereby minimize fresh water demand and effluent volume. However, it is possible that with such an approach the reductions in environmental impact brought about by saving water can be offset by other impacts arising from increased use of energy and materials in the plant after retrofit. This work presents a model for minimizing the total resource consumption impact of a water reuse/recycle network. The total impact is expressed in terms of emergy - a measure of cumulative solar energy inputs into a life cycle system. A simplified model is proposed that focuses on the impact contributions of water, electrical power and material for capital goods. Two case studies illustrate the approach. Results show that the network with the lowest total impact can be found by sacrificing water recovery for savings in energy and material use. © 2007 Curtin University of Technology and John Wiley & Sons, Ltd
Similar works
Full text
Available Versions
Animo Repository - De La Salle University Research
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:animorepository.dlsu.edu.p...
Last time updated on 03/12/2021