001.docx DOI: 10.3303/CET2189059 Paper Received: 6 June 2021; Revised: 14 October 2021; Accepted: 16 November 2021 Please cite this article as: Misrol M.A., Wan Alwi S.R., Lim J.S., Abdul Manan Z., 2021, Multi-objective Optimization of an Integrated Energy- Water-Waste Nexus for Eco-Industrial Park, Chemical Engineering Transactions, 89, 349-354 DOI:10.3303/CET2189059 CHEMICAL ENGINEERING TRANSACTIONS VOL. 89, 2021 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Jeng Shiun Lim, Nor Alafiza Yunus, Jiří Jaromír Klemeš Copyright © 2021, AIDIC Servizi S.r.l. ISBN 978-88-95608-87-7; ISSN 2283-9216 Multi-objective Optimization of an Integrated Energy-Water- Waste Nexus for Eco-Industrial Park Mohd Arif Misrol, Sharifah Rafidah Wan Alwi*, Jeng Shiun Lim, Zainuddin Abdul Manan Process Systems Engineering Centre (PROSPECT), Research Institute for Sustainable Environment (RISE), School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia syarifah@utm.my Compared to the linear economy, the circular economy promotes the minimization of fresh resources through the recovery of the applicable items. The concept of the circular economy can be implemented at the industrial level through the process systems engineering approach. A single element or multiple elements nexus can be developed in order to obtain an integrated optimal network. For a tripartite energy-water-waste nexus, focusing the wastewater as the subject of study, the optimization effort may not only be limited to the economic aspect, but it may also include the need to maximize the recovery of resource and the need to reuse and reclaim the wastewater stream. This will require the multi-objective optimization approach. In this study, a multi-objective optimization exercise to develop an integrated energy-water-waste is performed based on the fuzzy optimization constraints method. Three types of objective functions are assessed, namely to maximize profit, to maximize the amount of recovered resources, and to maximize the recovery of water. The mixed-integer non-linear programming (MINLP) model is formulated for such purposes. A case study conducted provides the solution that compromises the trade-off between the objective functions. An annual profit of 1.2 M USD/y, plus the recovery of biogas, struvite, metal hydroxides, and solid sludges, as well as the reuse and reclamation of 574 m3/h of water can be achieved. The model offers a perspective on how the economics and the environmental considerations can be optimized simultaneously. 1. Introduction The application of the circular economy will enable the minimization of the final waste and it also offers possibility to perform re-entry of the recovered resources into the ecosystem/supply chain. Certain resource is not actually a renewable item, for example, phosphorus (P); it is estimated that the P is production process from the phosphate rock will be ceased to be operated by the end of this century (Cooper et al., 2011). Through the application of the 6R strategies, namely reuse, recycle, redesign, remanufacture, reduce, and recover (Jawahir and Bradley, 2016), certain resources can be recovered from the process stream. Wastewater is known to possess certain type of contaminant that can be recovered via certain processes, either in the form of energy, water, and/or certain elements e.g. struvite. The idea to develop an integrated energy-water-nexus from wastewater is one of the researches to be explored. Misrol et al. (2020) explored the water-water nexus at the level of Eco-Industrial Park (EIP). Technically, the application of the process systems engineering (PSE) can be applied to achieve the circular economy goals (Avraamidou et al., 2020). The platform to perform the integration works is suggested to be at the Eco-Industrial Park (EIP) given the emphasizes to establish the industrial symbiosis among the participants, which it may not be promoted in the common industrial park. Misrol et al. (2021) explore the possible integration of domestic and industrial sources combined to recover biogas and to reuse and reclaim water for certain applications. Medeiros et al. (2021) use specifically yellow water as the main subject to form a water-energy-nutrient nexus. The application of tripartite nexus was also made by Kilkis and Kilkis (2017) in a diary facility. At residential level, the idea of the tripartite nexus was explored by Nunez Lopez et al. (2018). It was noted apart from the annual profit, there are also other items concerned, either to be maximized or to be minimized. These include the need to recover the resources as much as possible and the 349 need to minimize the usage of freshwater while retaining the profitability of the works. This will require the use of multi-objective optimization in order to find the relatively best solution for the multiple objective functions. In this study, a multi-objective optimization is performed based on the fuzzy optimization concept, which the degree of satisfaction (𝜆𝜆) is sought to be maximized. This paper is a continuation of the paper by Misrol et al. (2021b) which it explores the multi-objective optimization for the energy-water-waste nexus. In general, the multi- objective optimization is relatively common but, in this context, pertaining to the energy-water-waste nexus mentioned earlier, it is a novel concept/insight to look at. The method of the study is described in the Section 2 following. 2. Method The superstructure of this study is based on Misrol et al. (2021b), as shown in Section 2.1. Wastewater is the main subject of the study. It is desired to reclaim the water stream for reuse to the demand and to recover resources from the wastewater, which the optimization works can form an integrated energy-water-nexus for the EIP. The objectives in study are (i) to maximize the profit, (ii) to maximize the recovery of resources, and (iii) to minimize the consumption of freshwater. The mathematical formulations used in the model are written in Section 2.2. 2.1 Superstructure Figure 1 shows the superstructure of the study. There are 14 sets representing the applicable process sections and the contaminants, namely the water source (h), segregation (which the section segregates the streams based on the applicable treatment process and the potential of resources to be recovered) (i), water demand (j), food waste (k), regeneration (m), biogas (n), struvite recovery (o), ammonia recovery (p), metal recovery (q), aerobic digestion (r), microalgae (s), freshwater (w), outsourced water (os), and the contaminant contents (c). The possible resources to be recovered include biogas (for renewable electricity), solid digestate, struvite, ammonium sulphate, precipitated metal, and solid sludge. Other recoverable items are the reclaimed water and the waste heat from the gas engine. Figure 1: The superstructure of the study 350 There are four types of water demands, namely (i) boiler feed water (ii) process water, (iii) cooling water, and (iv) toilet flushing grade water. Each demand has different contaminant limits. The transportation of the involving streams is also considered (Misrol et al., 2021b). The main sources of revenue come from the supplying the water to the demand, selling the recovered resources, and processing fee the involving stream(s). 2.2 Mathematical formulation In this paper, the variables are written in italic and the parameters are in non-italic format. The background of the whole equations is referred from Misrol et al. (2021b). There are three objective functions of the study, namely (i) to maximize the annual profit generated from the integration works (𝑃𝑃𝑃𝑃), (ii) to maximize the annual recovery of resources from the wastewater (𝑅𝑅𝑅𝑅), and (iii) to maximize the volume of reused, reclaimed, and outsourced water (𝑊𝑊𝑅𝑅). Each objective function is written each in the following Eqs(1-3). 𝑅𝑅𝑅𝑅𝑅𝑅 is the annual revenue (USD/y) and 𝑇𝑇𝑇𝑇𝑇𝑇 is the total annualized cost (TAC) of the whole systems installed (USD/y), 𝐵𝐵𝐵𝐵 is amount of biogas produced (t/h) and 𝑆𝑆𝑆𝑆 is the amount of solid digestate recovered from the anaerobic digestion’s effluent (t/h). 𝑆𝑆𝑆𝑆𝑅𝑅 is amount of struvite precipitated (t/h) and 𝑇𝑇𝑆𝑆 is the ammonium sulphate produced (t/h). 𝑀𝑀𝑃𝑃 is the amount of metal hydroxides precipitated (t/h) and 𝑆𝑆𝑆𝑆 is the amount of solid sludge generated from the aerobic digestion process (t/h). 𝐹𝐹ℎ,𝑗𝑗 𝑠𝑠𝑠𝑠 is the directly reused water to the demand flow rate. 𝐹𝐹𝑖𝑖,𝑗𝑗 𝑠𝑠𝑠𝑠𝑠𝑠 is the flow rate of segregated greywater stream to the demand and 𝐹𝐹𝑚𝑚,𝑗𝑗 𝑟𝑟𝑠𝑠𝑠𝑠 is the regenerated water stream to the demand flow rate. 𝐹𝐹𝑞𝑞,𝑗𝑗 𝑝𝑝𝑝𝑝𝑠𝑠 is the flow rate of permeate generated from the metal recovery section to the demand and 𝐹𝐹𝑜𝑜𝑠𝑠,𝑗𝑗 𝑜𝑜𝑠𝑠𝑠𝑠 is the outsourced water flow rate to the demand. 𝑃𝑃𝑃𝑃 = 𝑅𝑅𝑅𝑅𝑅𝑅 − 𝑇𝑇𝑇𝑇𝑇𝑇 (1) 𝑅𝑅𝑅𝑅 = 𝐵𝐵𝐵𝐵 + 𝑆𝑆𝑆𝑆 + 𝑆𝑆𝑆𝑆𝑅𝑅 + 𝑇𝑇𝑆𝑆 + 𝑀𝑀𝑃𝑃 + 𝑆𝑆𝑆𝑆 (2) 𝑊𝑊𝑅𝑅 = ∑ 𝐹𝐹ℎ,𝑗𝑗 𝑠𝑠𝑠𝑠 ℎ,𝑗𝑗 + ∑ 𝐹𝐹𝑖𝑖,𝑗𝑗 𝑠𝑠𝑠𝑠𝑠𝑠 𝑖𝑖,𝑗𝑗 + ∑ 𝐹𝐹𝑚𝑚,𝑗𝑗 𝑟𝑟𝑠𝑠𝑠𝑠 𝑚𝑚,𝑗𝑗 + ∑ 𝐹𝐹𝑞𝑞,𝑗𝑗 𝑝𝑝𝑝𝑝𝑠𝑠 𝑞𝑞,𝑗𝑗 + ∑ 𝐹𝐹𝑜𝑜𝑠𝑠,𝑗𝑗 𝑜𝑜𝑠𝑠𝑠𝑠 𝑜𝑜𝑠𝑠,𝑗𝑗 (3) The equation for each type of recoverable resources is shown in Eqs(4-9). For the 𝐵𝐵𝐵𝐵 formulation in Eq(4), 𝐹𝐹𝑛𝑛 𝑏𝑏𝑠𝑠 is the flow rate of the wastewater stream into the biogas digester (m3/h) and 𝑇𝑇𝑛𝑛,𝑐𝑐 𝑏𝑏𝑠𝑠 is the contaminant content of the stream (g/m3). CODeff is the percentage of chemical oxygen demand (COD) removal efficiency (%) and CODyld is the conversion yield factor of biogas per kg of COD removed (m3/kg). CH4ct is the percentage content of methane in the raw biogas (%) and CH4d is the density of methane (kg/m3). CFgkg is the conversion factor from g to kg (1,000 g/kg). 𝐹𝐹𝑟𝑟𝑎𝑎𝑟𝑟is the flow rate of aerobic digestion process (m3/h) and 𝑇𝑇𝑟𝑟,𝑐𝑐𝑎𝑎𝑟𝑟 is the stream’s contaminant content (g/m3). 𝑇𝑇𝑟𝑟,𝑐𝑐𝑒𝑒𝑟𝑟 is the process’ effluent contaminant content (g/m3). RRrss is the percentage of sludge recyclability for each aerobic digestion process option (%) and ArTS is the percentage conversion of COD to Total Suspended Solids (TSS) (%). TSVSS is the percentage of Volatile Suspended Solids (VSS) relative to TSS (%) and VSSBg is the biogas yield per kg of VSS (m3/kg). VSSrmv is the percentage of VSS removal during the aerobic digestion process (%). In Eq(5), 𝐹𝐹𝑛𝑛 𝑠𝑠𝑠𝑠𝑠𝑠, in m3/h unit, is the solid digestate flow rate and 𝑇𝑇𝑛𝑛,𝑐𝑐𝑠𝑠𝑠𝑠 is the contaminant content of it. Specifically, the 𝑆𝑆𝑆𝑆 obtained is based on the total suspended solids (TSS) recovered from the 𝐹𝐹𝑛𝑛 𝑠𝑠𝑠𝑠𝑠𝑠 stream. CFgMT is conversion factor from g to t (106 g/t). 𝑆𝑆𝑆𝑆𝑅𝑅 recovery is as Eq(6); 𝑃𝑃𝑚𝑚𝑜𝑜𝑚𝑚 is the molarity of P in the stream and MWStv is the molar weight of struvite (g/mol). Formulation to recover 𝑇𝑇𝑆𝑆 is in Eq(7). 𝐻𝐻2𝑆𝑆𝑆𝑆4𝑚𝑚𝑜𝑜𝑚𝑚 is the molarity of sulfuric acid (H2SO4) used to neutralize the ammonium ion and MWAS is the molar weight of ammonium sulphate (g/mol). The recovery of metal hydroxides, specifically chromium (Cr), nickel (Ni), and Zinc (Zn) is formulated in Eq(8). 𝑇𝑇𝑃𝑃𝑚𝑚𝑜𝑜𝑚𝑚, 𝑁𝑁𝑁𝑁𝑚𝑚𝑜𝑜𝑚𝑚, and 𝑍𝑍𝑍𝑍𝑚𝑚𝑜𝑜𝑚𝑚 are the molarity of Cr, and Ni, and Zn each. The molecular weight of each metal is written as MWCr, MWNi, and MWZn each (g/mol). In Eq(9), 𝐹𝐹𝑟𝑟𝑎𝑎𝑟𝑟 is the influent flow rate of the aerobic digestion process (m3/h). 𝑇𝑇𝑟𝑟,𝐶𝐶𝐶𝐶𝐶𝐶𝑎𝑎𝑟𝑟 and 𝑇𝑇𝑟𝑟,𝐶𝐶𝐶𝐶𝐶𝐶𝑒𝑒𝑟𝑟 is the contaminant content of the influent and the effluent each (g/m3). RRrss is the constant regarding the recycling sludge into the aerobic process. ArTS is the COD conversion to TSS constant (TSS kg/COD kg) and TSSrmv is the percentage of TSS removal from the process (%). 𝐵𝐵𝐵𝐵 = ∑ �𝐹𝐹𝑛𝑛 𝑏𝑏𝑏𝑏×𝐶𝐶𝑛𝑛,𝐶𝐶𝐶𝐶𝐶𝐶 𝑏𝑏𝑏𝑏 ×CODeff×CODyld�𝑛𝑛 CFgkg×CH4ct×CH4d + �𝐹𝐹𝑟𝑟𝑎𝑎𝑟𝑟×�𝐶𝐶𝑟𝑟,𝐶𝐶𝐶𝐶𝐶𝐶 𝑎𝑎𝑟𝑟 −𝐶𝐶𝑟𝑟,𝐶𝐶𝐶𝐶𝐶𝐶 𝑒𝑒𝑟𝑟 �×(1−RRrss)×ArTS×TSVSS×VSSBg×VSSrmv� CFgkg + �𝐹𝐹𝑟𝑟𝑎𝑎𝑟𝑟×�𝐶𝐶𝑟𝑟,𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑟𝑟 −𝐶𝐶𝑟𝑟,𝑇𝑇𝑇𝑇𝑇𝑇 𝑒𝑒𝑟𝑟 �×TSSrmv×TSVSS×VSSBg×VSSrmv� CFgkg ; c=COD or TSS (4) 𝑆𝑆𝑆𝑆 = ∑ (𝐹𝐹𝑛𝑛 𝑠𝑠𝑠𝑠𝑏𝑏×𝐶𝐶𝑛𝑛,𝑐𝑐𝑠𝑠𝑠𝑠 )𝑛𝑛 CFgMT ; c=TSS (5) 351 𝑆𝑆𝑆𝑆𝑅𝑅 = 𝑃𝑃 𝑚𝑚𝑚𝑚𝑚𝑚×MWStv CFgMT (6) 𝑇𝑇𝑆𝑆𝑎𝑎𝑚𝑚𝑝𝑝 = 𝐻𝐻2𝑆𝑆𝐶𝐶4 𝑚𝑚𝑚𝑚𝑚𝑚×MWAS CFgMT (7) 𝑀𝑀𝑃𝑃 = ∑ �𝐶𝐶𝑟𝑟𝑚𝑚𝑚𝑚𝑚𝑚×MWCr×CPeff�+∑ �𝑁𝑁𝑖𝑖𝑚𝑚𝑚𝑚𝑚𝑚×MWNi×CPeff�+𝑞𝑞 ∑ �𝑍𝑍𝑛𝑛𝑚𝑚𝑚𝑚𝑚𝑚×MWZn×CPeff�𝑞𝑞𝑞𝑞 CFgMT (8) 𝑆𝑆𝑆𝑆 = �𝐹𝐹𝑟𝑟𝑎𝑎𝑟𝑟×�𝐶𝐶𝑟𝑟,𝐶𝐶𝐶𝐶𝐶𝐶 𝑎𝑎𝑟𝑟 −𝐶𝐶𝑟𝑟,𝐶𝐶𝐶𝐶𝐶𝐶 𝑒𝑒𝑟𝑟 �×1−RRrss)×ArTS� CFgkg + �𝐹𝐹𝑟𝑟𝑎𝑎𝑟𝑟×�𝐶𝐶𝑟𝑟,𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑟𝑟 −𝐶𝐶𝑟𝑟,𝑇𝑇𝑇𝑇𝑇𝑇 𝑒𝑒𝑟𝑟 �×TSSrmv� CFgkg (9) Eq(10) is regarding the annual revenue. 𝑅𝑅𝑅𝑅𝑅𝑅 is the combination of the annual revenue from selling (i) the supply water (𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝑊𝑊), (ii) the electricity generated from the biogas (𝑅𝑅𝑅𝑅𝑅𝑅𝐵𝐵𝑠𝑠), (iii) the solid digestate (𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝐶𝐶), (iv) the struvite (𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝑝𝑝𝑆𝑆), (v) the ammonium sulphate (𝑅𝑅𝑅𝑅𝑅𝑅𝐴𝐴𝑆𝑆), (vi) the precipitated metal hydroxides (𝑅𝑅𝑅𝑅𝑅𝑅𝑃𝑃𝑃𝑃), and (vii) the solid sludge (𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝑆𝑆). Total TAC is obtained through the summation of individual TAC work section of (i) the supply water (𝑇𝑇𝑇𝑇𝑇𝑇𝑆𝑆𝑊𝑊), (ii) biogas (𝑇𝑇𝑇𝑇𝑇𝑇𝐵𝐵𝑠𝑠), (iii) struvite (𝑇𝑇𝑇𝑇𝑇𝑇𝑆𝑆𝑝𝑝𝑆𝑆), (iv) ammonia (𝑇𝑇𝑇𝑇𝑇𝑇𝐴𝐴𝑚𝑚), (v) metal recovery (𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚𝑟𝑟), (vi) aerobic digestion (𝑇𝑇𝑇𝑇𝑇𝑇𝐴𝐴𝑟𝑟), (vii) pipeline (𝑇𝑇𝑇𝑇𝑇𝑇𝑝𝑝𝑚𝑚), (viii) water tank (𝑇𝑇𝑇𝑇𝑇𝑇𝑤𝑤𝑝𝑝), and (ix) disinfection (𝑇𝑇𝑇𝑇𝑇𝑇𝐶𝐶𝐷𝐷𝐷𝐷) as per Eq(11). 𝑅𝑅𝑅𝑅𝑅𝑅 = 𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝑊𝑊 + 𝑅𝑅𝑅𝑅𝑅𝑅𝐵𝐵𝑠𝑠 + 𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝐶𝐶 + 𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝑝𝑝𝑆𝑆 + 𝑅𝑅𝑅𝑅𝑅𝑅𝐴𝐴𝑆𝑆 + 𝑅𝑅𝑅𝑅𝑅𝑅𝑃𝑃𝑃𝑃 + 𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝑆𝑆 (10) 𝑇𝑇𝑇𝑇𝑇𝑇 = 𝑇𝑇𝑇𝑇𝑇𝑇𝑆𝑆𝑊𝑊 + 𝑇𝑇𝑇𝑇𝑇𝑇𝐵𝐵𝑠𝑠 + 𝑇𝑇𝑇𝑇𝑇𝑇𝑆𝑆𝑝𝑝𝑆𝑆 + 𝑇𝑇𝑇𝑇𝑇𝑇𝐴𝐴𝑚𝑚 + 𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚𝑟𝑟 + 𝑇𝑇𝑇𝑇𝑇𝑇𝐴𝐴𝑟𝑟 + 𝑇𝑇𝑇𝑇𝑇𝑇𝑝𝑝𝑚𝑚 + 𝑇𝑇𝑇𝑇𝑇𝑇𝑤𝑤𝑝𝑝 + 𝑇𝑇𝑇𝑇𝑇𝑇𝐶𝐶𝐷𝐷𝐷𝐷 (11) In this study, the multi-objective optimization method is based on the concept of fuzzy constraints optimization. The degree of satisfaction, namely lambda (𝜆𝜆), is the value that is intended to be maximized. The concept of 𝜆𝜆 regarding the multi-objective optimization is described by Tan et al. (2020). 𝜆𝜆 is incorporated in the fuzzy optimization constraints as per Eqs(12) – (15). 𝑃𝑃𝑃𝑃𝑚𝑚 and 𝑃𝑃𝑃𝑃𝑢𝑢 is the lower bound and upper bound of 𝑃𝑃𝑃𝑃 each. The same concept applies for the lower bound and upper bound of 𝑅𝑅𝑅𝑅 and 𝑊𝑊𝑅𝑅. 𝑃𝑃𝑟𝑟−𝑃𝑃𝑟𝑟𝑚𝑚 𝑃𝑃𝑟𝑟𝑢𝑢−𝑃𝑃𝑟𝑟𝑚𝑚 ≥ 𝜆𝜆 (12) 𝑅𝑅𝑅𝑅−𝑅𝑅𝑅𝑅𝑚𝑚 𝑅𝑅𝑅𝑅𝑢𝑢−𝑅𝑅𝑅𝑅𝑚𝑚 ≥ 𝜆𝜆 (13) 𝑊𝑊𝑅𝑅−𝑊𝑊𝑅𝑅𝑚𝑚 𝑊𝑊𝑅𝑅𝑢𝑢−𝑊𝑊𝑅𝑅𝑚𝑚 ≥ 𝜆𝜆 (14) 𝑀𝑀𝑀𝑀𝑀𝑀𝑁𝑁𝑀𝑀𝑁𝑁𝑀𝑀𝑅𝑅 𝜆𝜆; 0 ≤ 𝜆𝜆 ≤ 1 (15) 3. Case study A case study is conducted with based on the list of demands streams as in Table 1 and the water source and food waste streams as in the Table 2. The maximum flow rate of the demand is 800 m3/h each. Certain type of water source is imposed with certain amount of processing fee. Each type of water demand has different value of selling price. Other applicable parameters are referred from Misrol et al. (2021b). Each objective function was computed in separate run each. After that, the value of 𝑃𝑃𝑃𝑃, 𝑅𝑅𝑅𝑅, and 𝑊𝑊𝑅𝑅 were tabulated as it was then used to compute the maximum 𝝀𝝀 value. Table 1: Properties of the demands Streams Flow Rate (m3/h) Selling Price (USD/m3) Upper Bound Water demand – Boiler feed water Upper bound: 800 m3/h 1.42 Water demand – Process water Upper bound: 800 m3/h 1.35 Water demand – Cooling water Upper bound: 800 m3/h 0.71 Water demand – Toilet flushing grade water Upper bound: 800 m3/h 0.68 352 Table 2: Properties of the sources Streams Flow Rate (m3/h) Processing Fee (USD/m3) Water source – Ablution greywater 1 N/A Water source – Households greywater 125 N/A Water source – Blackwater 12.5 0.3 Water source – Total industrial boiler blowdown 81 N/A Water source – Total industrial cooling water blowdown 61 N/A Water source – Oil palm refinery effluents 2.6 1.2 Water source – Pharmaceutical plant effluents 120 1.2 Water source – Semiconductor industry greywater effluents 44.1 N/A Water source – Confectionary industry effluents 10 1.2 Water source – Chicken processing industry effluents 307 1.2 Water source – Milk industry effluents 17 1.2 Food waste 0.8 70 4. Results and discussion A computer with processor capacity of IntelCore i3-8130U 2.2 GHz was used to find the optimal solution and BARON is used as the solver. The maximum computation time was set at 1,000 s. The lower bound and upper bound value of 𝑃𝑃𝑃𝑃 each is 0.1 USD/y and 2.12 M USD/y. The lower bound and upper bound value of 𝑅𝑅𝑅𝑅 each is 0.49 t/h and 0.82 t/h. The lower bound of 𝑊𝑊𝑅𝑅 is 473 t/h and its upper bound value is 651 t/h. The optimal network selected is shown in Figure 2. Figure 2: The proposed optimization solution The directly reused water for process water and toilet flushing grade water is 15 m3/h and 113.2 m3/h each. The greywater is segregated for reuse at amount of 55 m3/h. 2.4 MW of electricity is generated based on the 353 anaerobic digestion of 379.3 m3/h stream. Struvite is then precipitated from the biogas effluent at amount of 105 kg/h. The struvite recovery effluent is further sent to the regeneration (379.2 m3/h). The metal recovery stream is sourced from the industrial sources with flow rate of 72.4 m3/h. The metal recovery process generates 44.5 m3/h of permeate that is reused for process water application and it also precipitates 14 kg/h of metal hydroxides. A total of 442.4 m3/h of freshwater and 1 m3/h of outsourced water are used for mixing purpose in order to supply process water and toilet flushing grade water to the demand. Total supply for each type of demand is 458.8 m3/h and 557.1 m3/h each. The annual profit obtained is 1.2 M USD/y and total amount of recovered resources is 0.68 t/h, which includes the biogas, struvite, metal hydroxides, and solid sludges. The volume of reused, reclaimed, and outsourced water streams combined is 573.5 m3/h. This corresponds to the 𝝀𝝀 value of 0.56. Table 3 provides the summary of the objective functions and the λ values obtained from the optimization exercise. Though the annual profit is relatively lower, the cumulative amount of recovered resources increases, and the amount reused, and reclaimed water remains substantial. The multi-objective optimization exercise based on the fuzzy optimization constraints approach compromises the needs to obtain the relatively best solution based different type of objective functions, which each may affect other adversely e.g., if profit is to be maximized, the amount of recovered resources may not be the maximum. The fuzzy optimization constraints method is relatively simpler although other method e.g. ε-method combined with the Pareto Optimal Front provides a range of optimal solutions for the multi-objectives optimization. It is intended to apply the latter approach into the next study 4. Conclusion In this study, a multi-objective optimization to develop an integrated energy-water-waste nexus is performed. The integrated network is able to reuse and reclaim the wastewater streams, which means the amount of freshwater can be minimized. The recovery of 2.4 MW of renewable energy, 105 dry kg/h of struvite, 14 dry t/h of metal hydroxides, 78 kg/h of solid sludge, and the freshwater reduction by 56 % can be achieved through the approach. The multi-objective optimization through the fuzzy optimization constraints method offers a trade-off between the individual objective functions. This study offers a perspective on how the intended goals can be optimized collectively so that the economic and the environmental benefits can be obtained simultaneously. Acknowledgements The authors thank Universiti Teknologi Malaysia (UTM) for funding the research presented in this paper via grant Q.J130000.2409.08G86, Q.J130000.21A2.04E44, Q.J130000.7709.4J375 and Q.J130000.3509.05G96. References Avraamidou S., Baratsas S.G., Tian Y., Pistikopoulos E.N., 2020, Circular Economy - A challenge and an opportunity for Process Systems Engineering, Computers & Chemical Engineering, 133, 106629. Cooper J., Lombardi R., Boardman D., Carliell-Marquet C., 2011, The future distribution and production of global phosphate rock reserves. Resources, Conservation and Recycling, 57, 78–86. Jawahir I.S., Bradley R., 2016, Technological elements of circular economy and the principles of 6R-based closed-loop material flow in sustainable manufacturing, Procedia CIRP, 40, 103–108. Kılkış Ş., Kılkış B., 2017, Integrated circular economy and education model to address aspects of an energy- water-food nexus in a dairy facility and local contexts, Journal of Cleaner Production, 167, 1084–1098. Lim J.S., Manan Z.A., Hashim H., Wan Alwi S.R., 2014, Synthesis of a sustainable integrated rice mill complex, Journal of Cleaner Production, 71, 118–127. Medeiros D.L., Kiperstok A.C., Nascimento F.R.A., Cohim E.H.B., Kiperstok A., 2021, Human urine management in resource-based sanitation: Water-energy-nutrient nexus, energy demand and economic performance, Sustainable Production and Consumption, 26, 988–998. Misrol M.A., Wan Alwi S.R., Lim J.S., Manan Z.A., 2021, An optimal resource recovery of biogas, water regeneration, and reuse network integrating domestic and industrial sources, Journal of Cleaner Production, 286, 125372. Misrol M.A., Wan Alwi S.R., Lim J.S., Manan Z.A., 2020, An optimal water-waste nexus for an eco-industrial park, Chemical Engineering Transactions, 81, 643-648. Núñez-López J. M., Rubio-Castro E., El-Halwagi M.M., Ponce-Ortega J.M., 2018, Optimal design of total integrated residential complexes involving water-energy-waste nexus, Clean Technologies and Environmental Policy, 20(5), 1061–1085. Tan Y.D., Lim J.S., Wan Alwi S.R., 2020, Multi-objective optimal design for integrated palm oil mill complex with consideration of effluent elimination, Energy, 202, 117767. 354 059.pdf Multi-objective Optimization of an Integrated Energy-Water-Waste Nexus for Eco-Industrial Park