Report on the WASTE-C-CONTROL Software Tool

Transcription

1 Report on the Software Tool Mdterm Report / Annex 7.3.3: Report on Software Tool 1

2 Contents 1 TOOL DEVELOPMENT METHODOLOGY MODEL BUILDING Waste Collecton Treatment Technologes Dsposal BASIC ELEMENTS OF THE MODEL Objectve functons Decson varables Constrants Parameters MODES OF OPERATION AND EXPECTED RESULTS SOFTWARE TOOL Appendx 1: Technologcal Optons ncluded n the Toοl (common confguratons) Appendx 2: Algorthms, Calculatons and Equatons Appendx 3: Operatonal Parameters Appendx 4: External Costs Mdterm Report / Annex 7.3.3: Report on Software Tool 2

3 1 Tool Development The man objectve of the project s to develop a software tool that asssts decson-makers to desgn and evaluate dfferent ntegrated waste management systems, from the level of waste generaton up to the level of waste treatment and dsposal, on the bass of overall GHG emssons and management cost. In addton, the evaluaton s extended also to ancllary envronmental mpacts, such as generaton of ar pollutants. The nvestgaton of dfferent WM alternatves s performed through mathematcal programmng, the outcome of whch s the dentfcaton of those ntegrated systems where ther overall GHG emssons can be reduced only at the expense of management cost. In other words, the tool asssts the user to buld the so-called Pareto fronter n the partcular stuaton faced. In ths way, decson-makers become aware of trade-offs nvolved n ther waste management problem and are able to select a soluton by beng aware of the man dlemmas and constrants faced. 1.1 Methodology The mathematcal model that descrbes the Muncpal Sold Waste (MSW) management system s developed usng the prncples of Mathematcal Programmng (MP). All the avalable technologes and paths of the MSW system are expressed n the model wth proper relatonshps (equaltes and nequaltes). The model conssts of the decson varables (the unknowns of the problem), the parameters (the known data), the constrants (the relatonshps that descrbe the system) and one or more objectve functons (the drvers of the optmzaton). Borrowng deas from the feld of process synthess n chemcal engneerng, the problem can be formulated as a mult-perod structure, desgn and operatonal optmzaton problem (Iyer and Grossmann, 1998). All the avalable MSW optons and ther nterdependences can be consdered n the superstructure of the system (topology of all the avalable MSW optons) and the MP model proposes the best soluton. A smultaneous, structural, desgn and operatonal optmzaton of the MSW system s acheved.e. the output of the created model s whch technology unts wll be used and whch paths are followed for the MSW system (structure), what s the capacty of these unts (desgn) and what are the flows and operatng loads to and from the unts (operatonal optmzaton). MP has already been used for the optmzaton of MSW systems n varous cases (see e.g. Abou Najm and El-Fadel, 2004, Lous and Shh, 2007, Jng et al., 2009). The model whch s developed s a mult-objectve mathematcal programmng model. Specfcally t has two objectve functons: (1) the Net Present Value of the system over the 20-year horzon and (2) the CO 2 -equvalent emssons. As the name suggests, mult-objectve optmzaton (or mult-crtera optmzaton) nvolves optmsaton n the presence of more than one (usually conflctng) objectve functons (crtera). The man dfference between sngle and mult-objectve optmzaton s that n the case of the latter, there s usually no sngle optmal soluton, but a set of equally good alternatves wth dfferent trade-offs, also known as Pareto-optmal (or non-domnated or effcent) solutons. The Pareto optmal solutons are the feasble solutons that cannot be mproved n one objectve functon wthout deteroratng ther performance n at least one of the rest. In the absence of any other nformaton, none of these solutons can be sad to be better than the other. Usually a decson maker s needed to provde addtonal preference nformaton and to dentfy the most Mdterm Report / Annex 7.3.3: Report on Software Tool 3

4 preferred soluton ( optmal accordng to hs/her subjectve preferences). Dependng on the paradgm used, such knowledge may be ntroduced before, durng or after the optmzaton process. Mult-objectve optmzaton thus has to combne two aspects: optmzaton and decson support (Steuer, 1986). In the present study, the generaton of the Pareto optmal solutons wll be done usng a verson of the popular epslon constrant method (Mavrotas, 2009). 1.2 Model Buldng The mathematcal model wll descrbe the MSW system as a drected graph. There are nodes that represent the processes and arcs that represent the flows between the processes. The boundares of the system are defned from the collecton phase tll the fnal dsposal. The model wll represent the superstructure of the system,.e. all the avalable optons wth ther nterconnectons as shown n Fgure 1. In Fgure 1, one can see how the bns are connected wth the processes, how the processes are nterconnected and whch the man products of each process are. It must be noted that for each generc technology there are more than one specfc type of unts that can be utlsed whch are mutually exclusve. For example, for Compostng we have 5 types of unts whle for MBT we have 18 types of unts. The optmal type of unt for each technology wll be selected by the model. The technologcal optons one may choose among, when dealng wth muncpal sold waste management, depend on whether wastes are source segregated or notthe followng optons were dentfed regardng waste collecton: Source separaton of dfferent fractons of dry recyclables (paper, glass, plastc, metals) Source separaton of a comngled stream of dry recyclables (all the materals aforementoned n one bn) Bn for commngled recyclables Source separaton of food waste - Bn for organc Mxed waste wthout source separaton mxed waste bn The model s properly formulated n order to perform structural, desgn and operatonal optmzaton. In other words, the major questons that wll be answered wth the optmzaton process are: whch processes (structure), what wll be ther capacty (desgn) and what wll be there annual operatonal load (operaton). All these fgures wll be computed n perod-wse bass. In techncal terms the model s a Mult-Objectve Mxed Integer Lnear Programmng (MO-MILP) model, whch means t contans contnuous and nteger (mostly bnary) varables. Mdterm Report / Annex 7.3.3: Report on Software Tool 4

5 Fgure 1: Graphcal representaton of the superstructure of the MSW system (Acronyms for Processes: TSR: Temporary Storage for Metals, Plastc and Paper, TSG: Temporary Storage for Glass, CMP: Compostng, AD: Anaerobc Dgeston, MRF: Materal Recycle Faclty, WtE: Waste to Energy, BD: Bodryng, MBT: Mechancal and Bologcal Treatment, TS: Transfer Staton, LDF1: Landfll, LDF2: Landfll for Hazardous Waste. Acronyms for products: ME: Metals, PL: Plastc, PA: paper, GL: Glass, CMP: Compost, BIOG: Bogas, DF: Derved Fuel, SO: Stablzed Organc, EN: Energy) The concept of collectng wastes, on whch the tool development was based, s that the user wll be able to choose among the followng collecton optons (see fgure 1): 1. One bn collecton system: a system wthout any source-separaton of materals, where all mxed waste s collected n a sngle bn, called mxed wastes bn 2. Two bn collecton system: a system wth source-separaton of co-mngled dry recyclables (paper, plastc, metals, glass) n one bn (bn for comngled ), and collecton of the rest wastes n the mxed wastes bn 3. Three-bn collecton system: bn for source-separaton of co-mngled dry recyclables (paper, plastc, metals, glass), bn for organc for source-separaton of organc wastes and collecton of the rest wastes n the mxed wastes bn 4. Mult-bn collecton system: combnaton of dedcated bns for source separaton of paper, metal, glass and plastcs and/or source separaton of co-mngled dry recyclables, wth/wthout source separaton of the organc fracton. Rest wastes are collected n the mxed wastes bn Mdterm Report / Annex 7.3.3: Report on Software Tool 5

6 Each system results n waste streams that may subsequently be treated n varous ways, as dentfed n Acton 1 and shortly descrbed n ANNEX of the Incepton Report. In order to create technologcally feasble confguratons, the avalable components were categorsed accordng to the followng: (a) Optons that are not always techncally meanngful or compatble (b) Optons that are techncally meanngful or compatble under specfc condtons (c) Optons that are always techncally meanngful or compatble Compatble and non-compatble combnatons are presented and dscussed n detal n ANNEX of the Incepton Report. Accordng to the above mentoned categorsaton, the most common confguratons for each opton were formulated. In ths way, the user may choose among a seres of confguratons accordng to ther needs. Each confguraton s a dfferent technology module n the tool so that the user may formulate a waste management plan that deals wth the varous streams wth a combnaton of modules. The treatment optons (the common confguratons have been slghtly altered to better reflect current trends n waste treatment, and the fnal flow charts of the technologcal optons ncorporated n the tool, are presented n Appendx 1). For each confguraton, mass flow calculatons had to be performed, as well as quantfcaton of emssons and consumptons. Also basc economc fgures had to be calculated, such as nvestment and operaton cost, as well as revenues from the sale of energy and recyclables / compost. The necessary modellng was orgnally ncluded n an Excel Workbook that utlses the data collected n Acton 1 and contans the necessary equatons to calculate all system materal and energy flows and generated envronmental burdens. Ths Workbook s the bass for the Software Tool so that necessary calculatons are performed. The concept of the modelng process s based on the fact that an ntegrated waste management system conssts of three general phases: waste collecton, sortng/treatment technologes and fnal dsposal. Wthn the context of ths project transportaton and storage of wastes and recovered materals are ntegrated n collecton and sortng/treatment technologes. For every operaton of the waste management system, a set of equatons and parameters that perform calculaton of operatonal parameters and materal and energy balances was defned Waste Collecton Waste collecton les at the centre of an ntegrated waste management system, as the way that waste materals are collected and sorted determnes whch waste management optons and technologes can be subsequently used. Waste collecton nfluences also the qualty of recovered materals as well. The term collecton ncludes not only the collecton of sold wastes from the varous sources, but also the haulng of these wastes to the locaton where the contents of the collecton vehcles are empted. In general there are two basc categores of waste collecton: Commngled (un-separated collecton) Mdterm Report / Annex 7.3.3: Report on Software Tool 6

7 Source separated collecton From the householder s vewpont comngled collecton of all sold waste together probably represents the most convenent method. Ths method, however, wll lmt the subsequent optons for treatment, due to the fact that most treatment technologes requre some form of separaton of the waste nto varous fractons at source. At ts smplest ths mght nvolve removng mxed dry recyclable materals, whle more extensve sortng nvolves the separaton of MSW nto several materal streams (glass, paper, metals, plastcs and organc fracton). The costs and major envronmental burdens assocated wth waste collecton systems wll be due to the nfrastructure (number of bns and vehcles) and the transport requred, whch consumes fuel and results n ar emssons. Gven the number of streams, the composton and quantty of each stream, the techncal characterstcs of the storage bns (capacty), the type of collecton vehcles (capacty and compacton) and the characterstcs of collecton scheme (frequency of collecton, average dstance between bns etc.) the requred number of bns and vehcles can be calculated, usng the above equatons: Where, Q V N = (1) and M r C f ( t 52) = ( ) / N C f (2) N = number of bns for stream M = number of collecton vehcles for stream Q = annual quantty of stream [lt. or kg] C = bn capacty of stream [lt. or kg] f = bn average fullness [%] t = number of collectons per week V = vehcle capacty of stream [lt. or kg] r = annual routes per vehcle. Annual routes per vehcle can ether be defned, usng real data from smlar operatng schemes, or may be calculated by defnng parameters of collecton scheme (unloadng tme for bns, average dstance and speed per route, workng days per year and workng hours per day etc.). Calculatng the number of routes requred for waste collecton and gven the dstance between bns and from/to a compacton, treatment or dsposal ste, total dstance and fuel consumpton for waste collecton can be easly calculated. It must be noted that for waste collecton vehcles, the stop-start nature of collecton and the compacton of waste materals make the use of standard heavy goods vehcle data napproprate, as the specfc fuel consumpton per km s hgher. Accordng to data from Greek muncpaltes the specfc consumpton of typcal waste collecton vehcles ranges from lt/km. Usng bns can also lead to a further source of burdens, due to the need to wash the bns, especally n the case of separate collecton of organc or comngled collecton. McDougal et al (2002) propose consumpton of 25 lt. of hot water and 0,6 kwh of fuel per bn and wash. Accordng to Greek specfcatons the frequency of washng s one tme per week for streams contanng organc fracton and one tme every sx months for source separated dry recyclable streams. Mdterm Report / Annex 7.3.3: Report on Software Tool 7

8 The economc data,.e. nvestment costs and costs for operaton and mantenance, are related to the type and number of the bns, the number of vehcles requred for the collecton and the total dstance travelled Treatment Technologes For all the waste treatment technologes a mass balance s calculated, both for each sold waste materal and total waste, consderng, where necessary, generated by-products, and losses. In general the output streams from each technology are: Recovered materals from dry recyclables (glass, metal, plastc and paper) Compost from bologcal treatment of source separated bowaste Compost lke output (or stablzed organc) from bologcal treatment of mxed waste fractons Derved fuel (RDF/SRF) Bogas, whch s combusted for power generaton Resdue of processes dsposed to landfll Mass losses due to mosture vaporzaton, combuston, bodegradaton of waste materals etc. External nputs Recycles Compost Feedstock Technology Losses Stablzed Organc Derved Fuel Bogas Landfll The mass balance for each waste materal and total waste s descrbed accordng to the followng equaton: where, F = R + C + S + DF + B + L + LF (3) F = annual waste feedstock of materal to the technology [tn/year] R = annual quantty of recovered dry recyclables from materal [tn/year] C = annual quantty of compost produced from materal [tn/year] S = annual quantty of compost-lke-output produced from materal [tn/year] Mdterm Report / Annex 7.3.3: Report on Software Tool 8

9 DF = annual quantty of derved fuel produced from materal [tn/year] B = annual quantty of bogas produced from materal [tn/year] L = annual quantty from materal n losses [tn/year] LF = annual quantty from materal n resdue [tn/year] For each output stream the annual quantty s calculated from an equaton lke the followng: Where, X = R, C, S, DF, B, and L X = F n (4) n = technology s recovery, transformaton or producton effcency for materal whch depends on operatons ncluded n the technology. The resdue to Landfll s calculated from equaton (3) after the calculaton of each one of the output streams by usng equaton (4). Data for recovery and producton effcency of varous treatment technologes are avalable n the database developed n Acton Dsposal The feedstock of landflls s the sum of the resdue from all treatment technologes calculated by usng equatons (3) and (4). Operatonal data, ar emssons and economcal data calculaton s accomplshed n a smlar way to treatment technologes. 1.3 Basc Elements of the Model The basc elements of the mult-objectve mathematcal programmng model are brefly descrbed below: Objectve functons Two are the objectve functons of the problem: (1) the mnmzaton of the Net Present Value (NPV) of the MSW system over a perod of 20 y, whch represents the economc objectve and (2) the mnmzaton of total CO 2 -eq emssons of the MSW system, whch represents the envronmental objectve. The NPV ncorporates the nvestment and operatonal costs, as well as the ncome from recyclables, electrcty and other products over a 20-year perod Decson varables The decson varables of the model are actually the unknowns of the problems,.e. those varables for whch we are tryng to fnd ther optmal values. In our case we have dscrete (bnary or nteger) and contnuous decson varables. The dscrete varables are mostly assocated wth the structural characterstcs (s -th technology present n the optmzed MSW Mdterm Report / Annex 7.3.3: Report on Software Tool 9

10 system? how many unts wll be needed?). The contnuous varables are mostly assocated wth the desgn and operatonal characterstcs (what s the capacty of -th unt n perod t? Whch s the amount of waste transported from -th unt to j-th unt?) Constrants The man constrants of the model are the mass balances that have to be satsfed between nodes (equalty constrants) and the capacty constrants that have to be satsfed ( less than constrants). There can be polcy constrants (e.g. the recyclng rate or the amount of waste sent to landfll). Logcal constrants are also present n order to apply condtons for mutually exclusve alternatves. Auxlary constrants may also be present (e.g. lnearzaton of nonlnear terms). Specal reference should be made to the modellng of the landfll and the assocated CH 4 emssons. It has been done usng the IPCC gudelnes and takes nto account fve waste categores (food, garden, paper, textle and wood) and the dfferent behavour of treated untreated materal. The calculaton of the CH 4 emssons (and therefore the CO 2 equvalent) takes nto account n a flexble manner the possblty of flarng, as well as the ongong process of CH 4 emssons after the expraton of the study horzon. These ex-post emssons are explctly calculated and partcpate n the mnmzaton of CO 2 -eq. objectve functon Parameters The parameters of the model are the known data. These data are the economc and technologcal characterstcs of the processes, the prces of the recycled materals and produced energy, and the converson factor of every ngredent n each one of the canddate technologes. The orgnal waste s classfed n 34 ngredents and ts composton s consdered known for the model based on representatve past data. The scheme of the bn confguraton s also consdered as gven (whch types of bns are used) n the model. The dfferent bn schemes can be examned as dfferent scenaros. Operatonal parameters for each technology such as land-take (footprnt) and power, fuel and water consumpton, whch were collected durng Acton 1, are expressed as ndces per tonne of feedstock. The general concept s that the software tool wll utlze these ndces to calculate technology operatonal data: Where, P = F (5) j p j P j = annual quantty of operatonal parameter j (e.g. fuel and power consumpton [kwh]) F = annual waste feedstock to treatment technology [tn/year] p j = operatonal parameter ndex (e.g. fuel and power consumpton [kwh/(tn/year)] The energy producton depends on the effcency of the equpment and the energy content of the fuel (bogas, derved fuel or waste stream n mass-burn ncneraton). Power and heat producton of Thermal Treatment and bogas combuston facltes are calculated n accordance to the followng equatons: P = f LHV n (6) and Q f = f LHV f nh (7), f f el Mdterm Report / Annex 7.3.3: Report on Software Tool 10

11 where LHV f = the heatng value of bogas or fuel (wastes or RDF), [kwh/tn of fuel or kwh/m3 of bogas] f: the quantty of fuel [tn of fuel/year or m3 of bogas/year] and n el and n h the power and heat producton effcences respectvely. In order to avod unnecessary approxmatons n calculatons of power and heat producton, a materal database, where each materal s assocated to a set of physcal propertes (densty, low heatng value, mosture, ash content and bogas producton yeld), has been developed. LHV of waste or derved fuel s calculated by usng materal specfc data of low heatng values and mosture: where, LHV = q LHV 1 m ) (8) f ( q = the percentage of materal n fuel [tonne of materal per tonne of fuel] LHV f = the heatng value of materal [kwh/tn] m = the mosture of materal n fuel [%] LHV of bogas s assumed to be 5,5 kwh/m3, assumng 55% methane content of gas. The amount of energy avalable for export usually depends upon the amount produced and the degree of self consumpton by the nstallaton and t s calculated by the followng equaton: P out = Pf Psf (9) and Qout Q f Qsf = (10), Where, P out = the power exported to the grd [kwh/year] Q out = the heat exported to end users [kwh/year] P f = the power produced from bogas or fuel [kwh/year] calculated wth equaton (6) Q f = the heat produced from bogas or fuel [kwh/year] calculated wth equaton (7) P sf = the power self consumed n the faclty [kwh/year] calculated wth equaton (5) Q sf = the heat self consumed n the faclty [kwh/year] calculated wth equaton (5). Resdual of thermal treatment facltes depends on ash content of the materals and t s calculated wth the followng equaton: where, R f = q a (9) q = the percentage of materal n fuel [tonne of materal per tonne of fuel] a = the ash content of materal [%]. Mdterm Report / Annex 7.3.3: Report on Software Tool 11

12 All relevant operatonal parameters were developed durng Acton 2 and have been nserted n the model n the form of text fles that are ncluded n Appendx 3. Ar Emssons, nclude drect process emssons of greenhouse gases (CO2, CH4 and N2O) and other ar pollutants such as PM10, NOx, SOx, PCDD/F, VOCs, NH3 and heavy metals, emssons due to fuel combuston, emssons due to transportaton of output streams and ndrect emssons due to power consumpton. A database of emsson factors for each pollutant assocated to feedstock or output and to fuel and power consumpton has been developed wthn the context of Acton 2b. All relevant data have been nserted n the model n the form of text fles that are ncluded n Appendx 3. Transportaton of the derved materals and resdue s ncluded n every technology. Dependng on the capacty and type of the vehcles and the dstance of end-users or landflls from treatment faclty fuel consumpton can be easly calculated. It must be noted that for the transportaton standard heavy goods vehcles are used. In nternatonal lterature there are wdely avalable data of specfc fuel consumpton (lt/km) for these type of vehcles. The economc data of each technology, such as nvestment cost, annual operatonal and mantenance cost, annual cost for the dsposal of resdue n landflls and rehabltaton cost are calculated from parameters related to the capacty of the process. These parameters are calculated by usng economc data collected n Acton 1. Revenues from sellng recovered materals, compost or energy produced are also taken nto account. All relevant data have been nserted n the model n the form of text fles (parameters) that are ncluded n Appendx 3. Wthn the scope of ths project external costs wll be also consdered. External costs or externaltes are defned as the costs and benefts whch arse when the socal or economc actvtes of one group of people have an mpact on another, and when the frst group fals to fully account for ther mpacts. Ths dscordance between the economc and the envronmental value system s due to the fact that most envronmental goods are not prced by exstng market mechansm whle decson makng and the prces of commodtes are usually formulated on the bass of prvate cost fgures only. External cost of waste management technologes may nclude mpacts related to ar polluton, mpacts related to clmate change, mpacts related to sol, groundwater and water resources contamnaton and mpacts related to dmnuton of lfe qualty due to odours, traffc congeston etc. All the relevant methodology for the ncorporaton of external costs n the Tool are descrbed n Appendx 4. The varous parameters (user nput or calculated) accordng to the waste management stage are presented below. Specfc algorthms for the calculated parameters are also presented. Sets (ndces) I: set of materals O: set of output from the processes J: set of bns P: set of processes T: set of tme perods Mdterm Report / Annex 7.3.3: Report on Software Tool 12

13 Table 1: User nput parameters - Collecton Symbol Descrpton Unts GAMS nput totwaste1 Total annual waste producton for 1st perod tn X p(i) Proporton of I-th materal n perod 1 % r(i,t) Rate of ncrease of materal I n perod T % bn(j) Indcator ( 0-1) parameter X pmatnbn(i,j) Percentage of materal I gong to bn J % bcap(j) Capacty of J-th type bn kg wf(j) Collecton frequency tmes per week for J-th bn - af(j) Average fullness for J-th bn % c_bn(j) Investment cost for J-th bn lt Lfetme of bn years Dscount factor % Table 2: Calculated parameters - Collecton Symbol Descrpton Unts Calculaton tquant(i,t) totwaste(t) q(i,j,t) sh(i,j,t) aq(j,t) CRF Amount of materal I n perod T Total quantty of waste n perod T Quantty of materal I, n bn J, n perod T Share of materal I n bn J n perod T Annual quantty of waste n J-th type bns Captal Recovery Factor Annual nvestment ac_bn(j) cost for J-th bn Annual bns of type J nbnc(j) needed per ton of nput Annual cost of bns bfcost(j) of type J per ton of nput tn tn tquant( I, T ) = totwaste1 p( I) (1 + r( I, T )) GAMS nput Mdterm Report / Annex 7.3.3: Report on Software Tool 13 T t= 2 N = X I= 1 totquant( T ) tquant( I, T ) tn q( I, J, T ) = pmatnbn( I, J ) tquant( I, T ) X % tn - sh( I, J, T) = q( I, J, T) N I= 1 N = I= 1 q( I, J, T) aq( J, T ) q( I, J, T ) lt (1 + ) CRF = lt (1 + ) 1 /year ac _ bn( J ) = CRF c _ bn( J ) 1000 nbnc( J ) = 52 wf ( J ) bcap( J ) af ( J ) - bf cos t( J ) = ac _ bn( J ) nbnc( J ) X Table 3: User nput parameters - Transportaton Symbol Descrpton Unts GAMS nput ad(p) Average dstance from bns to process km adldf(p) Average dstance from Process to Landfll km adbdwte Average dstance from BD to WTE km X admbtwte Average dstance from MBT to WTE km tl Truck load (12) t 5 X

14 Symbol Descrpton Unts dwh Workng hours per day (13) h ywd Workng days per year (315) d as Average speed (35 km/h usually) % cdt1 Collecton and dscharge tme from bns to processes (1.5) h cdt2 Collecton and dscharge tme from processes to landfll (0.5) h ovc overestmaton coeffcent (30%) - trc Truck nvestment cost trlf Truck lfetme years cpm Cost per klometer /km efpm Emsson factor of trucks n gco 2 /km (150) gco 2 /km Table 4: Calculated parameters - Transportaton Symbol Descrpton Unts Calculaton nt(p) ntldf(p) ntbdwte Number of trucks from bns to processes per ton of carred waste Number of trucks from processes to landfll per ton of carred waste Number of trucks from BD to WTE per ton of carred waste Number of trucks from MBT to WTE ntmbtwte per ton of carred waste Travelled dstance per year from bns to dpy(p) processes per ton of carred waste Travelled dstance per year from processes to dpyldf(p) landfll per ton of carred waste Travelled dstance per year from BD to WTE dpybdwte per ton of carred waste Travelled dstance per year from MBT to dpymbtwte WTE per ton of carred waste 1/tn 1/tn 1/tn 1/tn km/tn km/tn km/tn km/tn atrc Annual truck cost tc(p) Total annual transportaton cost 2 ad ( P) + cdt1 nt( P) = as tl dwh ywd 2 adldf ( P) + cdt2 ntldf ( P) = as tl dwh ywd 2 adbdwte + cdt2 ntbdwte= as tl dwh ywd 2 admbtwte + cdt2 ntmbtwte= as tl dwh ywd 2 ad( P) dpy( P) = tl 2 adldf ( P) dpyldf ( P) = tl 2 adbdwte dpybdwte= tl 2 admbtwte dpymbtwte= tl trlf (1 + ) atrc= trc trlf (1 + ) 1 GAMS nput GAMS nput /tn tc( P) = atrc nt( P) + cpm dpy( P) X Mdterm Report / Annex 7.3.3: Report on Software Tool 14

15 Symbol Descrpton Unts Calculaton from bns to processes per ton of carred waste Total annual transportaton cost tcldf(p) from processes to landfll per ton of carred waste Total annual transportaton cost tcbdwte from BD to WTE per ton of carred waste Total annual transportaton cost tcmbtwte from MBT to WTE per ton of carred waste Emsson factor from eftbp(p) transportaton from bns to processes Emsson factor from eftpl(p) transportaton from processes to landfll Emsson factor from efbdwte transportaton from BD to WTE Emsson factor from efmbtwte transportaton from MBT to WTE GAMS nput /tn tcldf ( P) = atrc ntldf ( P) + cpm dpyldf ( P) X /tn tcbdwte= atrc ntbdwte+ cpm dpybdwte X /tn tcmbtwte= atrc ntmbtwte+ cpm dpymbtwte X /tn eftbp( P) = efpm dpy( P) X /tn eftpl( P) = efpm dpyldf ( P) X /tn efbdwte= efpm dpybdwte X /tn efmbtwte= efpm dpymbtwte X Table 5: User nput parameters - Processes Symbol Descrpton Unts GAMS nput lnk(p,j) Indcator varable lnkng bn J to process P - X capmn(p) Mnmum capacty for process P tn X capmax(p) Maxmum capacty for process P tn X nvmn(p) Investment cost for mnmum capacty for process P /tn nvmax (P) Investment cost for maxmum capacty for process P /tn pvcost(p) Annual operaton cost for process P /tn X plf(p) Lfetme of process P y Dscount factor % cc(p,i,o) Converson factor of materal I n output O n process P - X rfemldf(t) Reducton factor for emssons of landfll for perod T - X el_eff Electrc effcency for WTE unt - X efproc(p) Emsson factor from process P (per tn nput) kgco 2 /tn X Mdterm Report / Annex 7.3.3: Report on Software Tool 15

16 Table 6: Calculated parameters - Processes Symbol Descrpton Unts Calculaton Captal CRF(P) Recovery Factor Annual nvestment acmn(p) cost for mnmum capacty unt Annual nvestment acmax(p) cost for maxmum capacty unt Slope for the annual acslope(p) nvestment cost Intercept for the annual acntercept(p) nvestment cost - CRF( P) = (1 + ) plf ( P) plf ( P) (1 + ) 1 acmn( P) = capmn(p) nvmn(p) CRF ( P) acmax(p)= capmax(p) nvmax(p) CRF ( P ) /tn acmax(p)- acmn(p) acslope(p)= capmn(p)- capmax(p) GAMS nput acntercept(p)= acmn(p)- acslope(p) capmn(p) X X Table 7: User nput parameters - Materals Symbol Descrpton Unts GAMS nput prce_(i) Prce of I-th materal /tn X prce_o(o) Prce of O-th output (maybe negatve=cost) /tn X el_prce Prce of electrcty /MWh X ncv(i) Net calorfc value of materal I MWh/tn X rectarget(i) Recycle target for materal I % X The entre set of equatons and calculatons s presented n Appendx 2. Mdterm Report / Annex 7.3.3: Report on Software Tool 16

17 1.4 Modes of Operaton and Expected Results The model can be used as an optmzaton tool or just as a smple calculaton tool. The user can adjust the extent of optmzaton by controllng the degrees of freedom of the model. Instead of performng a full optmzaton (wth all the degrees of freedom), he/she can consder some technologes as gven and the system wll be optmzed gven ths nformaton. In ths case the correspondng decson varables wll have fxed values n the optmzaton and wll not be altered. Moreover, the user can mpose constrants (user defned constrants) on the flows (e.g. no more than 30,000 tn/year can be sent from the mxed waste bn to the MBT unts). The full model ncludes approxmately 24,445 contnuous varables, 210 nteger varables and 12,399 constrants. The optmzaton of the mult-objectve model provdes a representatve set of the Pareto optmal solutons for the MSW management problem. Wth the term soluton we mean the structural characterstcs (whch unts wll be constructed n each perod), the desgn characterstcs (the capacty of the unts, what capacty expansons wll be requred) and the operatonal characterstcs (annual waste flows between the unts). All these amounts are expressed wth approprate decson varables and ther values wll be the man output of the system, of course along wth the value of the objectve functon(s). The algorthm that we use for the generaton of the Pareto optmal solutons s the augmented ε-constrant (AUGMECON) (Mavrotas, 2009). The model s multperod and has a dynamc evolvng element over tme, followng the scenaro for the quantty of produced MSW (20y horzon dvded nto four perods). The results of the optmzaton wll refer to each perod of tme and there wll be nter-perod constrants quantfyng the relevant lnkng relatonshps. The model has been mplemented and solved usng the wdely known modellng language GAMS / General Algebrac Modellng System (Brooke et al., 1998). Mdterm Report / Annex 7.3.3: Report on Software Tool 17

18 2 Software Tool The Waste-C-Control tool s a decson support software (DSS) targeted to experts and/or practtoners n the feld of MSW management. The user s able to examne varous scenaros by desgnng conceptual archtectures of MSW systems by selectng among the component technologes. Dependng on the selected component technologes the system creates the conceptual archtecture by addng n the graphcal map the respectve type of bns n the collecton system, the types of landflls for dsposal followng the superstructure constrants llustrated n Fgure 1 and the lnks among the component technologes (Fgure 2). Note that for the dsposal we use two types of landfll, wth the hazardous connected only wth the Waste to Energy component. For each technology (.e. compostng, anaerobc, WtE, etc.), many types of processes have been ncorporated. Therefore, the software contans an extensve lbrary wth cost data (capex, opex) and envronmental data (emssons, fuel consumpton, etc.), for each technology type. Data has been drawn from lterature, but also from questonnares dstrbuted to operatng plants n Greece and Europe. Fgure 2: Conceptual map of an MSW system Subsequently, the user has to defne the parameters of the MSW system: (a) the composton of the MSW generated, (b) the collecton system parameters,.e. bn models, collecton frequency, etc., (c) the component technologes parameters, e.g. mn/max capactes of unts n tonnes per year, etc., and (d) the transportaton parameters, e.g. truck models of each lnk, average dstances, etc. Next, the user may defne constrants to be consdered n the optmzaton problem, e.g. recyclng targets, restrctons on the number of unts, or n the flows between component technologes and run the optmzaton. The outcome of the optmzaton s the Pareto fronter ; n ths case a curve wth the x-axs representng the net present cost for the 20y horzon and the y-axs the envronmental mpact of each soluton, thus tons of CO 2 -eq. (Fgure 3). The user may select a pont on the Pareto curve and see the parameters and outcomes of the respectve soluton analysed n the four 5-year perods (Fgure 4). Mdterm Report / Annex 7.3.3: Report on Software Tool 18

19 Fgure 3: Pareto curve for the feasble solutons of an MSW system Fgure 4: Summary of the outcomes of a feasble soluton Mdterm Report / Annex 7.3.3: Report on Software Tool 19