(442f) A Market Clearing Framework for the Evaluation of Municipal Waste Management and Recycling Economies | AIChE

(442f) A Market Clearing Framework for the Evaluation of Municipal Waste Management and Recycling Economies

Authors 

Tominac, P. - Presenter, University of Wisconsin-Madison
Zavala, V. M., University of Wisconsin-Madison
Modern supply chain optimization models have grown to encompass global markets, numerous stakeholders, and multiple objectives [1,2]. Independent stakeholders in a supply chain require sufficient compensation for goods and services rendered, an axiom known as revenue adequacy and which is assured in coordinated market clearing models [3]. An important result of this process is that market prices are determined by bids rather than prescribed by the operator, who does not otherwise participate in the market. Market clearing is used in electricity generation to assign portions of consumer demand to power generators who communicate bidding information to an independent system operator, the problem of which has seen considerable analysis in electrical engineering literature [4,5].

Of emerging interest within the chemical engineering community is the problem of waste management, and in particular the development of circular economies for municipal, industrial, and agricultural waste streams containing valuable materials [6,7,8]. A common objective in waste management supply chains is the identification of a suitable network of waste sources, transport vectors, and technological processes suitable to recover value from materials otherwise destined for landfill. An important aspect of such models is the understanding that the individual providers of waste material, transportation, and processing are often independent stakeholders.

We present a market clearing framework for supply chain modeling which ensures that every active participant is allocated adequate revenue. Our framework is based on the collection of bidding information from buyers and sellers of goods and services in a market and uses this information to enact transactions. As a result, we obtain market clearing prices from dual variable information following model solution, providing intuition about the nature of the market and the costs associated with each element. We present case studies based on a municipal waste management problem in which recycling processes are made available to recover the valuable fractions of a separated waste stream, and evaluate the prices resulting at each stage of the market.

References
[1] Garcia DJ, You F. Supply chain design and optimization: Challenges and opportunities. Comput Chem Eng, 2015; 81: 153-170.
[2] Liu S, Papageorgiou LG. Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry. Omega, 2013; 41(2): 369-382.
[3] Zavala VM, Kim K, Anitescu M, Birge J. A stochastic electricity market clearing formulation with consistent pricing properties. Oper Res, 2017; 65(3): 557-576.
[4] Bohn RE, Caramanis MC, Schweppe FC. Optimal pricing in electrical networks over space and time. Rand J Econ, 1984: 360-376.
[5] Kazempour J, Pinson P, Hobbs BF. A stochastic market design with revenue adequacy and cost recovery by scenario: Benefits and costs. IEEE T Power Syst, 2018; 33(4): 3531-3545.
[6] Mohammadi M, Jämsä-Jounela SL, Harjunkoski I. Optimal planning of municipal solid waste management systems in an integrated supply chain network. Comput Chem Eng, 2019; 123: 155-169.
[7] Santibañez‐Aguilar JE, Martinez‐Gomez J, Ponce‐Ortega JM, Nápoles‐Rivera F, Serna‐González M, González‐Campos JB, El‐Halwagi MM. Optimal planning for the reuse of municipal solid waste considering economic, environmental, and safety objectives. AIChE J, 2015; 61(6): 1881-1899.
[8] Guo M. Multi-scale system modelling under circular bioeconomy. Comput-Aided Chem En. 2018; 43: 833-838.