Automated Targeting Technique for Single-Impurity Resource Conservation Networks. Part 2: Single-Pass and Partitioning Waste-Interception Systems

Abstract

Part 1 of this pair of articles presents an automated targeting technique to identify minimum fresh resource flow rate/cost targets in a resource conservation network (RCN) with material reuse/recycle. After the potential for conservation through direct reuse/recycle is exhausted, fresh resource consumption can be further reduced by incorporating waste-interception (regeneration) processes. Hence, the proposed automated targeting technique in part 1 of this pair of articles is extended to determine the targets for RCNs with interception placement. The waste-interception systems are modeled as treatment processes with either fixed outlet concentrations or fixed impurity load removal ratios. The approach also distinguishes between single-pass and partitioning regenerators, which have different implications for RCNs. Literature examples and industrial cases are solved to illustrate the proposed approach.

Synthesis of Material Interception Networks with P-Graph

“…The basic P-graph structure shall follow the recently established structure as proposed by Lim et al (2017). Note that there are two general types of interception unit, i.e., single pass and partitioning types (Tan et al 2009;Ng et al 2009b). Both types of interception units can be further classified as fixed outlet quality or fixed removal ratio, based on their performance (Foo 2012).…”

Synthesis of Heat-Integrated Resource Conservation Networks

“…Various works for more generalised fixed flowrate problem were also reported for direct reuse/recycle (Dhole et al, 1996;Polley and Polley, 2000;Hallale, 2002;El-Halwagi et al, 2003;Manan et al, 2004;Prakash and Shenoy, 2005;Bandyopadhyay et al, 2006;Foo et al, 2006b;Almutlaq and ElHalwagi, 2007;Foo, 2007;Shenoy and Bandyopadhyay, 2007;Foo, 2008) and for regeneration systems (Ng et al, 2007a;Ng et al, 2008b). Furthermore, the use of combined conceptual-based and mathematical optimisation techniques was also reported for water network synthesis with reuse/recycle, regeneration and total water network (Alva-Argáez et al, 2007;Ng et al, 2007b;Ng et al, 2007c;Ng et al, 2009a;Ng et al, 2009b;Ng et al, 2010). Note that total water network consists of the elements of water reuse/recycle, regeneration (treatment for reuse/recycle), and waste treatment (treatment for final discharge).…”

“…Kazantzi and El-Halwagi (2005) and Foo et al (2006a) extended the conventional targeting techniques developed for concentration-based RCN into property integration problem. Besides, Ng and co-workers extended their automated targeting techniques (Ng et al, 2009a;Ng et al, 2009b) for property-based RCN problems too (Ng et al, 2009c;Ng et al, 2010). Concurrently, there is also a small group of methods that focus on batch processes (Grooms et al, 2005;Ng et al, 2008a;Chen et al, 2010).…”

MILP Model for the Nonlinear Problem of Optimal Purifier Placement

“…Foo and Manan (Foo and Manan 2006) evaluated two different purification processes with given feed concentration by the GCA technique. Ng et al (Ng et al 2009) employed an automated targeting approach to address this kind of purification problem.…”