New approach for optimization of construction and demolition waste management

Transcription

1 New approach for optimization of construction and demolition waste management Application to the Lisbon Metropolitan Area António Rebello de Andrade Mestrado em Engenharia Civil ABSTRACT: The growing concern regarding global sustainability is increasingly evident in the construction sector due to the impact of waste production. The aim of this wor, which follows the one of Correia (2013), is to develop a new optimization approach in order to plan a networ for recycling Construction and Demolition Waste (CDW). The approach is based on the methodology of Processes and Systems Engineering (PSE), allowing better problem systematization, visualization of the CDW flows between processes and better planning of the needs of the networ. The developed model, of the mixed integer linear programming (MILP) type, is a tool to support CDW management in the assessment of the recycling networ from two points of view: from a regulatory perspective that aims to minimize the total costs of the networ and from the perspective of transforming entities, which aim to maximize profit in the recycling processes. It is also possible to design diverse scenarios and perform sensitivity analyses of model parameters that mae the assessment more clear regarding location, types and capacity of new processes to be installed. The model was validated for a 10 parish sample and then explored for the 211 parishes that comprise the Metropolitan Area of Lisbon (MAL). However, due to its generic formulation, the model can be applied at regional or national level. The main results indicate a preference for direct deposition in landfills and the fact that high quality recycling processes are not economically viable for the MAL. 1. Introduction Construction and demolition waste (CDW) represents 25-30% of the total amount of waste in the EU (Malia et al. 2011) and due to its distinct morphological characteristics its management is made difficult. Many strategic guidelines have been taen towards reducing the environmental, social and economic impacts generated by this type of waste. European concerns regarding CDW management led to a change in the legal framewor, which establishes that by 2020 all member states should have at least 70% of non-hazardous CDW recycled or reused. Portugal, unlie other EU countries, is far from this goal with only 9% of CDW recycled, 11% reused, 4% incinerated, whereby 76% of CDW produced in Portugal goes directly to landfill (Coelho & Brito, 2012). This wor follows that of Hiete et al. (2011) and applied by Correia (2013) to MAL, where the development of optimization models to properly design CDW recycling networs operationalizes the strategic positions taen. A new approach to optimize the planning of CDW recycling networ to cover the current needs of CDW management was developed, maing it a relevant tool to support 1

2 decision maing. The derived mathematical formulation is rigorous due to the approach that specifies the processes used, allowing greater detail in the flow visualization, a better design of the networ and more expeditious detection of existing errors in networ planning. The model was validated for a 10 parish sample and then applied to the 211 parishes that constitute the MAL (parish distribution before 2013) and conclusions were drawn. 2. Optimization model for the planning of a CDW recycling networ The optimization model for the planning of a CDW recycling networ is based on the principles of processes and systems engineering (PSE). However, time was not taen into account as a model variable (processes were not scheduled in time). 2.1 Problem definition A model based on the principles of PSE can be integrated to predict the overall behavior of the system, detect failures in the networ and to test the result of design alternatives or changes in the process. This new approach results in the breadown of processes in recycling facilities as presented in Figure 1. Figure 1 - Illustrative scheme of each facility processes The facility "Recycling Plant" encompasses three processes (screening, LQ and HQ recycling) and the facility "Sorting Plant" includes only the screening process. The Mixed Integer Linear Programming (MILP) model considers simply: Four types of materials: CDW, Intermediate Products (IP), Sold Products (SP) and Residual Material (RM); Networ nodes with attributes (production of CDW, number and type of processes, number of landfills); Cost of: transport (depending on the distance between nodes), landfilling of CDW and RM, processing of CDW and the IP, investment in new processes (depending on capacity), sales of SP (income); Two main objective functions: minimizing the overall cost of the recycling networ (objective of the regulator entity) and profit maximization of the recycling process (objective of the transforming entities). According to the objective function to be studied the model presents results on: Installation of new processes (type and capacity); Display of the material flow in the networ, meaning the amount of material transported between each pair of nodes (input and output materials for different processes); 2

3 Figure 2 shows the final layout of all processes and materials considered. In order to understand each process of this layout the State Tas Networ (STN) methodology referred in Duque et al (2010) is used. Screening S1 S2 MR1 S5 S6 S7 S8 Screening (1 ou 2) MR2 S3 S4 Materials S1 - Concrete S2 Undifferentiated material S3 Screened Concrete S4 Screended undifferentiated S5 Mixtures of metals S6 - Wood S7 - Plastic S8 Paper and paperboard S9 Undifferentiated LQ S10 LQ concrete S11 - HQ concrete S12 Mat. processed in landfill MR1 Residual Material 1 MR2 - Residual Material 2 S12 Landfill (5) Recycling MR2 S4 LQ Recycling (3) S5 S6 S7 S8 S3 S9 S10 MR2 HQ Recycling (4) S5 S6 S7 S8 S11 Figure 2 - Final layout of the processes Note: S1 and S2 materials can only have the following sequence S1S3S10S11 S2S4S9. and The CDW management options considered are: recycling or direct landfilling of CDW (S1 and S2). For the material to be recycled it must first be screened: this can be done by a sorting plant (1) or a 3

4 screening incorporated into the recycling plant (2). This is then followed by the LQ recycling process (3) and optionally by the HQ recycling process (4), since the S10 materials (LQ concrete) can continue to 4 process or be sold. Residual material (RM) and CDW enter the landfill process (K5) and the output is S Mathmatical formulation of the model Before the implementation of the model, the formal model formulation is presented. Therefore, based on the problem description, the following sets, parameters and variables are defined: Sets and indices I Networ nodes K - Processes KC Maximum capacities considered for the processes S Material states RCD subset of CDW PI Subset of intermediate products PV- Subset of sold product s MR Subset of residual materials Is,s Input s material in process Os,s Output s material in process i, j I, K c Kc s, s S RCD S PI S PV S MR S Parameters b i,j material transportation costs from node i to node j ( /ton) i, j I cda s Landfilling cost of s( /ton) s RCD MR e s Sale value of s ( /ton) s PV pp s Value that CDW producers pay to treat waste ( /ton) s RCD ū s Global demand of material s PV (ton/year) i I s PV hsi, - Quantity of material s produced at node i (ton/year) i I s RCD V,s,s Proportion of material s obtained from material s in process K s, s S ȳ,i Existing processes at node i K i I ȳ,i =1 for existing process at i and ȳ,i =0 otherwise Cp,s Processing cost of material s in process ( /ton.year) s RCD Kup,i Existing process capacity of process at node i K i I c,c Available capacity c to install new process (ton/year) K c KC 4

5 Ic,c Investment costs ( ) installation with capacity of c for process K c KC Variables Custo Total cost of CDW recycling networ CustoProcesso Cost of CDW in recycling processes CustoAterro Deposit cost in landfill of CDW LucroProcesso Profit of recycling processes Q s,i,j,, Quantity of material s that comes from process at node i and enters on process at node j (ton/year) if j=i material s stays at the node. if j i material s is transported between nodes X s,i, Quantity of material s sold at node i from process (ton/year) Y, i Binary variable of existence of the process in node i y,i =1 if process is installed at i and y,i =0 otherwise Yc, c, i - Binary variable for selection of capacity c to install in process at node i Yc, c, i =1 if capacity c in process at node i is installed and Yc, c, i =0 otherwise Ki,i Installed capacity for process in node i (pre-existing and new) Ii,i Investment cost associated with installation of process in node i Auxiliary variables for interpretation of results To now the input material at recycling process srcd ii ji 0 ( 1 2) Q s, i, j,, Z processo To now the input material for direct landfilling srcd ii ji 0 5 Q s, i, j,, Z aterro Objective functions Using the above definitions, the model is formulated as follows: Cost of recycling process CustoProcesso b cp * Q cda b * Q j, i, s s, i, j,, s j, i s, i, j,, ji ii Os, s sis, s ( 0, 5) ji ii 5sMR Os, s X * e Iii, s,, i s ( 0, 5) ii s( Oss, PV ) 0, 5 ii [1] Landfilling cost cd s j, i * [2] s, i, j,, CustoAterro a b Q ji ii 5sRCD Os, s 5

6 Total cost Custo CustoProcesso CustoAterro [3] Profit of the recycling process Lucro Qs, i, j,, * pp Custo Pr ocesso ji ii 1, 2sRCD 0 s Constraints [4] Existing processes cannot be closed: Y, i ȳ,i 0, 5i I [5] Choice of the capacity range Yc, c, i for the new process installed at i: Yc, c, i Y, i se yi, 0 [6] c If ȳ,i =0: Definition of the capacity Ki,i of new process installed at i and if ȳ,i =1: definition of the capacity Ki,i of existing process installed at i: Ki Kc * Yc i, 0 se y 0, i, c, c, i, i c Ki up i, se y 1, i, i, i [7] Investment cost for installing a new process at location i: Ii Ic * Yc se y 0 [8], i, c, c, i i, c Upper limit to sale of material s: X s,, i ūs s PV S [9] K ii The materials production is limited by the capacity of the process installed at location i: Qs, i, j,, Ki, i i, ' 0 [10] sis, s ji Os, s Initialization of the material flow (mass balance for CDW materials at each node i): Qs, i, j,, ' hs, i i, 0, s RCD [11] ji ' 1, 2 Mass Balance for each node i and every material s that results from process : v Q X Q i 0 s Os [12], s, s' s, j, i,, s', i, s', i, j,, ', s sis, s Os, s ji ' Is, s ji Expression [3] is the total cost, to be minimized, and is subdivided into processes costs and landfilling costs. Expression [1], cost of the recycling process, is the first part of expression [3] and includes the operating cost of the various processes including the cost of transport (first term), the 6

7 cost of landfilling of residual materials, including transportation cost (second term), the sales value of products sold (third term) and the investment cost of a new process (fourth term). Expression [2] is the cost of direct landfilling of CDW. Expression [4] is the profit of the recycling process, to be maximized, because the viability of waste management and operation of private entities requires consider this factor. For that, the value that the CDW producers pay to recycle waste (which is a transforming entity income) is diminished by the cost that processors have with the recycling process. Equation [5] ensures that existing processes can not be closed. Binary variable Y, i (which reflects the existence of a process at node i) only allows process to be installed in each node. Expression [6] only happens when there are no preinstalled processes in node i, ie when ȳ,i =0, and allows the selection, if it leads to an optimal solution, of only a c capacity to be installed in i. Both variables in the equation are binary. Expression [7] defines the Ki, variable. When ȳ,i =1 (process exists in i), Ki,i variable assumes the value of Kup,i, the value of the capacity of process in i. When ȳ,i =0 (there is no process in i) the Ki,i variable assumes the capacity to be installed if it has been previously selected with Yc,c,i variable in equation [6]. Equation [8] defines the Ii,i variable (investment cost associated with process in i) occurring only when a new process is installed (ȳ,i =0) and only taes a nonzero value if it was selected one of c capacities available to install the process in i (if Yc,c,i =1). Expression [9] restricts the sale of s9, s10 and s11 products to the existing demand, and for materials s5, s6, s7, s8 no demand was applied. Expression [10] restricts the processing capacity of the processes. The mass balance equation of the CDW in each node i shown in equation [11], while arises from the need to initialize each node i with the CDW values produced by the "virtual process" 0. This process is considered virtual and was not detailed in this wor, due to the lac of consistent data on the CDW production process/demolition. So equation [11] is used to initialize the mass balance of the process. The equation of global mass balance [12] intends to eep in balance the quantities of material in the system in each node i, for each process and material s'. 3. Results and discussion The model was programmed using GAMS modeling language and implemented on an Intel Core i7 CPU, 4700MQ, with 2.40 GHz and 8 GB of RAM. The model formulation is a mixedinteger linear programming problem, applied to MAL, and it was solved with CPLEX which uses a branch and bound algorithm to reach optimization. 3.1 Scenarios and sensitivity analysis The scenarios analyses used, compares the solutions with and without legal imposition. As an example, the analysis of scenario B (for total cost minimization), which considers the non-existence of processes in MAL, is presented. The CDW production was estimated by Bernardo (2013), the 7

8 distribution of CDW flows gathered from Coelho (2012) and other data collected by the author of the paper. Figure 3 displays the material flows for this scenario. 418,28 Kton 179,26 Kton S2 S1 Scenario B with legal enforcement 418,28 Kton 179,26 Kton 597,54 Kton Landfill (5) Kton ,28 Kton 179,26 Kton Figure 3 - Material flows in scenario B S2 S1 Scenario B without legal enforcement 155,86 Kton 23,4 Kton 262,42 Kton 155,86 Kton 179, ,31 Kton Landfill (5) S1 0 Kton S2 0 Kton 1 S1 155,86 Kton S2 262,42 Kton 2 MR1 81,19 Kton MR2 43,12 Kton 106,64 Kton 418,28 Kton S ,93 Kton S9 S3 131,34 Kton S4 168,66 Kton 4 Scenario 1 Under "normal" conditions of the scenario all the material goes directly to landfill and recycling processes are not installed given that this management option is not economically advantageous (left hand side of figures). When applying the legal enforcement, where at least 70% of the CDW must be recycled, it is observed the installation of 2 and 3 in node i171 ( ton/year) and 2 in node i22 ( ton/year), in the solution which optimizes distribution processes in MAL (right hand side of figure 3). Table 1 presents a summary of the main results for the various scenarios studied. Scenarios A-E mae the comparison of solutions with and without legal enforcement. For scenarios F-Q a sensitivity analysis of the critical parameters of the model was performed, while a 20% variation in the parameter values (positive and negative). Parameters Constraints Table 1 - Main results obtained in the various scenarios Nº Processes Total Cost Total Processing (M ) Cost (%) cost (M ) Cost of direct disposal (M ) Direct disposal rate (%) Recycling rate (%) A , ,62 11,65 93,6 6,4 A* ,16 139,85 13,25 3, B absence of processes ,41 101, , B* absence of processes ,89 162,10 15,87 4, C Installs K ,15 107,17 1,5 11,65 93,6 6,4 C* Installs K ,04 147,03 14,13 3, D Kup,i , ,62 11,65 93,6 6,4 D* Kup,i ,99 146,62 14,08 3, E h s,i ,04 89,98 1,03 10,01 89,34 10,66 E* h s,i ,91 113,37 10,02 3, F (+20%) b i,j ,25 107,99 0,68 12,56 93,55 6,45 G (-20%) b i,j ,29 92,01 0,53 10,76 94,01 5,99 H (+20%) u s , ,62 11,65 93,6 6,4 1 The symbol * meansthatthelegalenforcement has been imposed of at least 70% of recycled waste. 8

9 I (-20%) u s , ,62 11,65 93,6 6,4 J (+20%) Cda(S1,S2) ,68 111,49 0,70 12,98 92,72 7,28 K (-20%) Cda(S1,S2) ,85 88,43 0,52 10,33 94,65 5,35 L (+20%)Cda(mr1, mr2) ,36 100,73 0,57 11,79 95,05 4,95 M (-20%)Cda(mr1, mr2) ,14 98,94 0,78 11,36 90,31 9,69 N (+20%) e s ,19 99,34 0,73 11,46 91,46 8,54 O (-20%) e s ,33 100,49 0,57 11,76 94,74 5,26 P (+20%) Ic,c , ,62 11,65 93,6 6,4 Q (-20%) Ic,c , ,62 11,65 93,6 6,4 For detailed analysis of scenarios A to E, it is concluded that, under current conditions, the direct disposal of CDW in landfill is more economically viable than the recycling processes. When legal enforcement is applied, the solution that minimizes costs is also the option of minimum recycling (70%). S2 material, which represents the major flow of CDW, is not a worthwhile material to recycle due to the high costs involved when compared to S1 material. The high capacity of existing processes leads to the non-favorable installation of new processes in the studied region. The processing in 4 is unviable and this is accentuated with the analysis of scenario C, where despite forcing the installation of this process, the S10 material is sold instead of entering the HQ recycling process. This reflects what occurs in MAL as there is no process of this type installed. In the remaining sensitivity analysis, results are consistent with the variation applied (20% positive or negative). The minimum cost solution is the one of scenario K (10,85M ), in which the landfilling costs of S1 and S2 had a 20% decrease. The maximum cost solution without legal enforcement (13,68M ) occurs in scenario J, that corresponds to an increase of 20% in the parameter mentioned above. To conclude, landfilling cost is the parameter that influences most the total cost of the CDW recycling networ. 4. Conclusions The developed mixed integer linear programming model, in addition to the versatility present in the evaluation of results from the perspective of a regulatory and a transforming entity, enables decision-maing regarding the installation of new processes (location, type of process and capacity). Due to the breadown of processes in facilities it is possible to control flows in the processing of CDW and so optimize recycling networ. Regarding the minimization of total costs of the recycling networ, direct deposition in landfills is the CDW management option which implies lower costs, and as such is preferred to recycling. In many countries the increase in disposal costs in landfills proved to be a favorable measure to increase the recycling rate as confirmed in this wor with the sensitivity analysis of this model parameter. In order to meet the goal set by the EU for 2020, the analysis of various scenarios was 9

10 considered, comparing solutions which include a legal enforcement, where at least 70% of the CDW must follow recycling processes. This legal requirement may be a measure to be applied by a regulatory authority and witch can combine the rising cost of landfill to achieve the 2020 objectives in a more sustainable manner. A further conclusion is that the HQ recycling process does not contribute to the reduction of total networ cost due to the lac of flow in this process developed in various scenarios. In the application related to the CWD transforming entities, it was possible to assess the potential of the developed mathematical formulation. It will be interesting, in future developments, to deepen this perspective in addition to implementing the environmental assessment component of global sustainability analysis of the CDW recycling networ. References Bernardo, M., Gestão dos resíduos de construção e demolição: caracterização de resíduos e processos. Dissertação para obtenção do grau de Mestre em Engenharia Civil, Instituto Superior Técnico, Universidade de Lisboa. Coelho, A., Análise de viabilidade de implantação de centrais de reciclagem de resíduos da construção e demolição em Portugal - PARTE III - Análise de viabilidade de uma central de reciclagem. Relatório no âmbito de bolsa de pós-doutoramento. Universidade Técnica de Lisboa, Instituto Superior Técnico. Coelho, A., Brito, J. de, Economic viability analysis of a construction and demolition waste recycling plant in Portugal - part I: location, materials, technology and economic analysis. Journal of Cleaner Production, V.39, pp Correia, M., Optimização da gestão de resíduos de construção e demolição: Aplicação à Área Metropolitana de Lisboa. Dissertação para obtenção do grau de Mestre em Engenharia Civil, Instituto Superior Técnico, Universidade de Lisboa. Duque, J., Barbosa-Póvoa, A., Novais, Design and Planning of Sustainable Industrial Networs: Application to a Recovery Networ of Residual Products. Industrial & Engineering Chemistry Research, V. 49 nº 9, pp Hiete, M., Stengel, J., Ludwing, J., Schultmann, F., Matching construction and demolition waste supply to recycling demand: a regional management chain model. Building Research & Information, V.39, nº4, pp Mália, M., Brito, J. de, Bravo, M., Indicadores de resíduos de construção e demolição para construções residenciais novas. Ambiente Construído, Porto Alegre, V. 11, nº 3, pp

11 11