(185b) Development of a Spatio-Temporal Multi-Objective Optimisation Model for Multi-Product Oil Palm Value Chains

Oil palm is a versatile and high-yielding crop that can be used to produce a wide variety of products, including oleochemicals, energy and materials [1]. The oil palm fruit, called fresh fruit bunch (FFB), can be milled to produce crude palm oil (CPO) and kernel palm oil (KPO), which are the main feedstocks for the production of oleochemicals and biodiesel. The resulting “waste” streams from the oil production can be further processed into valuable products. The palm oil mill effluent (POME) can be fed into anaerobic digesters to produce biomethane, which can be used to generate heat and electricity. The empty fruit bunches (EFB), on the other hand, can be converted to: syngas via gasification; bio-oil via pyrolysis; bioethanol via lignocellulosic fermentation; fibre mat via mechanical treatment; composts and other valuable products.

In this conference, we will present a spatio-temporal mixed integer linear programming (MILP) model that can simultaneously determine the design and operation of multi-product value chains [2-3] for oil palm. A value chain is a set of activities required to convert raw materials into products and services, including pre-processing and pre-treatment, conversion to intermediates and final products, logistics, inventory and waste management [4]. The model is used to examine and optimise different scenarios in the oil palm sectors in Peninsular Malaysia. In order to capture the spatial-dependencies of the problem, such as the candidate locations for oil palm plantations and processing facilities, and location of demands, Peninsular Malaysia is represented as a grid of 50 km squares. The candidate locations for oil palm plantations are modelled in GIS in order to exclude forests, peatlands and other land areas whose utilisation may result in emissions of stored CO₂. The model considers a long planning horizon, out to 2050, in order to model the staged investment in and retirement of technologies. The model determines interdependent decisions such as where to locate the plantations and processing facilities, when to invest in them and what size/capacity; what products to produce, how to transport and store resources, centralised or distributed production, among others. Different objectives are considered such as maximisation of profit and minimisation of GHG emissions. Pareto sets are generated in order to determine optimal solutions that represent a balance between economic gain and environmental protection.

References:

[1] A. Abdulrazik, M. Elsholkami, A. Elkamel, L. Simon (2017). Multi-products productions from Malaysian oil palm empty fruit bunch (EFB): Analyzing economic potentials from the optimal biomass supply chain, Journal of Cleaner Production, 168, 131-148, 2017.

[2] S. Samsatli, N.J. Samsatli (2017). A multi-objective MILP model for the design and operation of future integrated multi-vector energy networks capturing detailed spatio-temporal dependencies. Applied Energy. DOI: 10.1016/j.apenergy.2017.09.055.

[3] S. Samsatli, N.J. Samsatli, N. Shah (2015). BVCM: a comprehensive and flexible toolkit for whole-system biomass value chain analysis and optimisation - mathematical formulation. Applied Energy, 147, 131-160.

[4] S.M. Jarvis, S. Samsatli (2018). Technologies and infrastructures underpinning future CO₂ value chains: a comprehensive review and comparative analysis. Renewable & Sustainable Energy Reviews, 85, 46-68.