Kathleen B. Aviso

Fuzzy optimization of topologically constrained eco-industrial resource conservation networks with incomplete information

It is possible to minimize industrial resource consumption by establishing eco-industrial resource conservation networks (RCN) between different plants. The establishment of these networks requires the satisfaction of quality criteria for material properties deemed significant by an industry. It also necessitates cooperation among the different firms based on the satisfaction of individual cost or resource consumption goals. Furthermore, there may be varying degrees of incomplete information regarding the process data of the participating plants. Eco-industrial RCNs may also be topologically constrained with respect to the number of links connecting different plants. These design aspects are incorporated in the optimization model through fuzzy mixed integer linear programming (FMILP) or fuzzy mixed integer non-linear programming (FMINLP). Case studies from literature involving water integration and hydrogen recovery are used to illustrate the methodology. The model is able to identify the topologically constrained network that achieves the highest level of overall satisfaction among participating plants.

Fuzzy mixed-integer linear programming model for optimizing a multi-functional bioenergy system with biochar production for negative carbon emissions

A multi-functional bioenergy system is an efficient way for producing multiple energy products from biomass, which results in near-zero carbon emissions. To achieve net negative carbon emissions, biochar production as carbon sequestration can be integrated in the system. A fuzzy mixed-integer linear programming model is developed to simultaneously design and optimize a multi-functional bioenergy system given multiple product demands, carbon footprint, and economic performance constraints. Case studies are presented involving multi-functional bioenergy systems with biochar production for carbon sequestration. The results show that net negative carbon footprint can be achieved in such systems.

A VULNERABILITY INDEX FOR POST-DISASTER KEY SECTOR PRIORITIZATION

Input–output-based techniques have proven to be effective in modeling how disasters lead to economic disruptions, while taking into account the structural connectivity of economic systems. In particular, through the inoperability input–output model (IIM), the degree of failure in an economic system can be quantified on a scale from 0 (normal state) to 1 (complete failure). This paper develops a vulnerability index that builds upon the foundations of the Leontief input–output model and the IIM, which is capable of identifying and prioritizing the key sectors in the aftermath of disasters. The key sector prioritization framework proposed in this paper is expected to contribute to the domain of disaster preparedness planning, such as enhancing the efficiency of resource allocation across various sectors. The proposed vulnerability index is formulated in terms of three underlying components: (1) economic impact, (2) propagation length, and (3) sector size. The vulnerability index captures the impact of investments to various sectors in times of disaster in order to yield the maximum benefits to the entire economy. This paper considers a baseline scenario that assumes that the decision-maker has an equal preference for all index components. Using Monte Carlo simulation and sensitivity analysis, we investigated the extent to which the key sector rankings could fluctuate with respect to variations in the decision-maker preferences. Key sectors tend to be sensitive to the weight assignments across the three vulnerability index components; nevertheless, some sectors are less sensitive to such weight variations and may persist on their level of priority, independent of the scenario. Using the Philippine input–output data, we found that the private services sector is consistently a high-priority sector, the trade sector is a mid-priority sector while the real estate and ownership of dwellings sector tend to be a low-priority sector.

Simultaneous carbon footprint allocation and design of trigeneration plants using fuzzy fractional programming

Trigeneration systems offer an inherently efficient, low-carbon approach to producing useful energy streams. Due to multiple products from a trigeneration system, the challenge of allocating carbon footprint to each energy stream arises, particularly if the streams are sold to different customers. A fuzzy fractional programming model is proposed to design a trigeneration system, taking such allocation into account. The model allows for solving for a configuration that gives the minimum carbon footprint for each energy stream, given a range of values for demand for each product in a trigeneration system. The final design must meet a specified energy output requirement, while satisfying fuzzy carbon footprint limits for all products. The methodology is illustrated using hypothetical but realistic case studies. Sensitivity analysis was carried out to show the effects of changing the system carbon footprint limits.

P-graph and Monte Carlo simulation approach to planning carbon management networks

Abstract A P-graph and Monte Carlo simulation approach to planning carbon management networks is proposed. These networks are generalized systems for minimizing emissions of CO 2 . Application of the P-graph framework to such problems has the added advantage of being able to rigorously identify both optimal and near-optimal solutions, which is a feature that is useful for practical decision-making; Monte Carlo simulation can then be used to evaluate the robustness of a network to variations in system parameters. Two literature case studies are used to demonstrate this methodology. The first example is a carbon-constrained energy sector planning problem, while the second example is a CO 2 capture and storage planning problem. In both cases, it is demonstrated that multiple solutions generated using P-graph methodology allow identification of robust, near-optimal carbon management networks.

An extended P-graph approach to process network synthesis for multi-period operations

Abstract An extension of the P-graph approach for multi-period process network synthesis (PNS) is proposed in this work. A modification of a previously published approach enables partial load operational lower limit for process units to be considered via the addition of fictitious streams. A simple case study is presented to illustrate the advantages of this modified approach.

An Inverse Optimization Approach to Inducing Resource Conservation in Eco-Industrial Parks

Abstract The exchange of wastes among plants within an eco-industrial park (EIP) creates potential for significant gains in sustainability through efficient use of resources and reduction of environmental discharges. If the establishment of such resource conservation networks (RCNs) is not economically optimal, intervention of an EIP authority will be necessary in order to induce companies to act in an environmentally responsible manner. This conflict of interest between the EIP authority and the industrial plants results in a Stackelberg game, which may be represented as a bi-level optimization model. In this work, a bi-level linear integer programming model for optimizing waste exchange in an EIP is developed. Then, an inverse optimization approach is used to solve it. An auxiliary model is used to determine the best set of incentives and disincentives to induce the plants in the EIP to form an optimal RCN. The methodology is demonstrated using an illustrative case study.

A SHOCK ABSORPTION INDEX FOR INOPERABILITY INPUT–OUTPUT MODELS

Recent disasters have underscored the importance of enhancing resilience in economic systems. In this work, we propose a novel shock absorption index, which provides a measure of the ability of an economic system to tolerate disruptions. It is assumed that there are externally defined initial levels of system failure or disruption, as well as maximum allowable levels of inoperability for each sector. The shock absorption index is defined as the largest fraction of the anticipated initial disruption that can be absorbed by the predefined robustness limits. It provides an overall measure of the robustness of an economic system towards a disruptive event, which is driven by both the economic structure and the individual robustness of different sectors. The results of two case studies illustrate policy-making insights in identifying and prioritizing risk management strategies for critical systems.

A Fuzzy Programming Approach to Multi-Objective Optimization for Geopolymer Product Design

Abstract Geopolymer is an inorganic polymer binder formed from the alkaline activation of reactive alumino-silicate materials resulting in two- or three-dimensional polymeric network. It is a promising alternative to Portland cement-based materials because of its lower embodied energy and carbon footprint with potential for waste valorization. Studies have been done to develop such material with desired engineering specification by using statistical design of experiment and optimizing the process conditions or mix formulation of waste materials. However, it is not only the engineering properties such as its mechanical and thermal properties, but also other properties pertaining to green materials (e.g., embodied energy and carbon footprint) have to be considered. Conflicting objectives may also have to be satisfied simultaneously to find a compromised solution in the product design such as that of maximizing the strength and minimizing the volumetric weight. This work thus proposes a weighted max-min aggregation approach to multi-objective optimization of the geopolymer product using fuzzy programming approach. The optimization formulation was introduced such that fuzzy sets represent both the aspired product desirability and soft constraints; the optimal mix is then found by maximizing the simultaneous satisfaction of target properties of the desired product. This work also proposes an extension of such fuzzy optimization formulation wherein the nature of trade-off between improving the product desirability and satisfying the fuzzy constraints are made explicit. The relative importance of the properties as represented by priority weights were derived systematically using Analytic Hierarchy Process (AHP). A case study on a ternary blended geopolymer from coal fly ash, coal bottom ash, and rice hull ash is presented to illustrate the proposed method.

Fuzzy Multi-Objective Approach for Designing of Biomass Supply Chain for Polygeneration With Triple Footprint Constraints

Polygeneration systems produce multiple energy products (i.e. electricity, heat, cooling), and other biochemical products (biofuels and syngas). Such systems offer a sustainable approach in meeting the ever-growing demand of energy, while reducing its environmental impact. The optimal design of such systems should consider the design of the supply-chain in producing the targeted energy products to reduce the resource consumption and waste generation and to maximize its economic potential. One of the important considerations in designing such a system is whether to out-source its raw materials or to produce them in-house. The criteria for such decision strategies are assessed through economics, product demand, and environmental impact. One holistic way to measure the environmental impact of such system is to consider the triple footprint: carbon, water, and land. The objective of this work is to maximize the economic potential while maintaining the footprints at acceptable levels and simultaneously meeting product demands. In this study, an adoption of fuzzy multi-objective approach is presented wherein the economic potential is introduced as a constraint. Moreover, predefined fuzzy trapezoidal-shaped limits for the product demand constraints are used which mimics the probabilistic demand scenario for each of the product streams. Lastly, the triple footprint constrains is utilized to assess the environmental impact of the polygeneration. The technique is demonstrated using a modified industrial case study of a polygeneration system.Copyright