A Synthesis of Optimization Approaches
for LCA-Integrated Industrial Process
Modeling: Application to Potable Water
Production Plants
Florin Capitanescu, Antonino Marvuglia and Enrico Benetto
Abstract This paper synthesizes the authors’ experience in the area of integrated
approaches coupling multi-objective optimization (MOO), industrial process
modeling and simulation, and life cycle assessment (LCA), with particular application to the sector of drinking water production. An industrial process is intended
as any process using a certain technology to produce a product or deliver a service.
The paper discusses comparatively the suitability for the optimization of a
real-world drinking water production plant (DWPP) of four optimization approaches, namely: (1) off-the-shelf global search metaheuristic algorithms, (2) hybrid
optimizers combining global search and local search, (3) surrogate model based
optimizers, and (4) local search.
1 Introduction
The combination between various optimization methods and life cycle assessment
(LCA) has been initiated two decades ago [1], with the aim to empower decision
makers with Pareto trade-off cost-effective solutions to decrease environmental
impacts of processes. Many approaches have been proposed since then (e.g. [2–4])
in this research area; the reader is referred to [5] for a relatively comprehensive
survey. Rooted in the same research field, but focusing on the computationally
expensive optimization problem of eco-design of drinking water production plants
(DWPPs), the 3-year project “Optimization based integrated process modellingLCA: application to potable water production” (OASIS) has further explored the
best paths for the threefold coupling (process modeling, LCA and optimization)
along four major optimization research streams namely: (1) off-the-shelf global
search metaheuristic optimization algorithms [6], (2) hybrid optimizers combining
global search and local search [7], (3) surrogate model based optimizers [8, 9], and
(4) local search [10].
F. Capitanescu (&) Á A. Marvuglia Á E. Benetto
Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
e-mail: florin.capitanescu@list.lu
© The Author(s) 2018
E. Benetto et al. (eds.), Designing Sustainable Technologies,
Products and Policies, https://doi.org/10.1007/978-3-319-66981-6_3
21
for LCA-Integrated Industrial Process
Modeling: Application to Potable Water
Production Plants
Florin Capitanescu, Antonino Marvuglia and Enrico Benetto
Abstract This paper synthesizes the authors’ experience in the area of integrated
approaches coupling multi-objective optimization (MOO), industrial process
modeling and simulation, and life cycle assessment (LCA), with particular application to the sector of drinking water production. An industrial process is intended
as any process using a certain technology to produce a product or deliver a service.
The paper discusses comparatively the suitability for the optimization of a
real-world drinking water production plant (DWPP) of four optimization approaches, namely: (1) off-the-shelf global search metaheuristic algorithms, (2) hybrid
optimizers combining global search and local search, (3) surrogate model based
optimizers, and (4) local search.
1 Introduction
The combination between various optimization methods and life cycle assessment
(LCA) has been initiated two decades ago [1], with the aim to empower decision
makers with Pareto trade-off cost-effective solutions to decrease environmental
impacts of processes. Many approaches have been proposed since then (e.g. [2–4])
in this research area; the reader is referred to [5] for a relatively comprehensive
survey. Rooted in the same research field, but focusing on the computationally
expensive optimization problem of eco-design of drinking water production plants
(DWPPs), the 3-year project “Optimization based integrated process modellingLCA: application to potable water production” (OASIS) has further explored the
best paths for the threefold coupling (process modeling, LCA and optimization)
along four major optimization research streams namely: (1) off-the-shelf global
search metaheuristic optimization algorithms [6], (2) hybrid optimizers combining
global search and local search [7], (3) surrogate model based optimizers [8, 9], and
(4) local search [10].
F. Capitanescu (&) Á A. Marvuglia Á E. Benetto
Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
e-mail: florin.capitanescu@list.lu
© The Author(s) 2018
E. Benetto et al. (eds.), Designing Sustainable Technologies,
Products and Policies, https://doi.org/10.1007/978-3-319-66981-6_3
21