• Energy Research
• 13. Climate action close

The industrial sector has a large presence in world energy consumption and CO2 emissions, which has made it one of the focal points for energy and resource efficiency studies. However, large investments are required to retrofit existing industrial plants, which remains the largest barrier to implementing energy saving solutions. Process integration methods can be used to identify the best investments to improve the efficiency of plants, yet their timing remains to be answered using an optimisation approach. Even more critically, such decisions must also account for future investments to avoid stranded or regretted investments. This paper presents a method incorporating investment planning over long time horizons in the framework of process integration. The time horizon is included by formulating the problem using multiple investment periods. Investment planning is conducted using a superstructure approach, which permits both commissioning and decommissioning of units in the beginning of each period. The method is applied to a large case study, with an industrial cluster neighbouring an urban centre to also explore options of heat integration between industries and cities. Compared to the business-as-usual operation, optimal investment planning improves the operating cost of the system by 27% without budget constraints and 16–26% with constraints on budget and investment periods, which is reflected as an increase in net present value and a decrease in CO2 emissions. In all cases, the operating cost benefits pay off the investment in less than two years. The present work is efficient in finding energy saving solutions based on the interest of industries. This method adds additional perspectives in the decision-making process and is adaptable to various time horizons, budgets and economic constraints.

Efficient consumption of energy and material resources, including water, is the primary focus for process industries to reduce their environmental impact. The Conference of Parties in Paris (COP21) highlighted the prominent role of industrial energy efficiency in combating climate change by reducing greenhouse gas emissions. Consumption of energy and material resources, especially water, are strongly interconnected and, therefore, must be treated simultaneously using a holistic approach to identify optimal solutions for efficient processing. Such approaches must consider energy and water recovery within a comprehensive process integration framework which includes options such as organic Rankine cycles for electricity generation from low–medium-temperature heat. This work addresses the importance of holistic approaches by proposing a methodology for simultaneous consideration of heat, mass, and power in industrial processes. The methodology is applied to a kraft pulp mill. In doing so, freshwater consumption is reduced by more than 60%, while net power output is increased by a factor of up to six (from 3.2 MW to between 10–26 MW). The results show that interactions among these elements are complex and therefore underline the necessity of such comprehensive methods to explore their optimal integration with industrial processes. The potential applications of this work are vast, extending from total site resource integration to addressing synergies in the context of industrial symbiosis.

Abstract Due to the global increase in energy consumption, greenhouse gas emissions, and the depletion of fossil energy resources, the research presented here is focused on finding economically and environmentally competitive renewable energy resources. Fuel production from biomass is an attractive solution in this regard. Competing interests between food and energy have yielded increased interest in lignocellulosic biomass (LGB) as a feedstock. Processes such as biodiesel production from palm oil generate large volumes of LGB residues. Valorization of these residues through biorefineries may bring economic and environmental benefits through substitution of fossil fuels and such options must be studied in a systematic manner. The goal of this research is to propose a methodology for economic and environmental analysis of such biorefineries. A case study of a palm-based biorefinery in Brazil is used to illustrate this. Results indicate that multi-product processes can yield significant cost and environmental benefits.

The concept of Positive Energy Districts (PEDs) has emerged as a promising approach to achieving sustainable urban development. PEDs aim to balance the energy demand and supply within a district while reducing the carbon footprint and promoting renewable energy sources. Urban–Industrial Symbiosis (UIS) is another approach that involves the exchange of energy and resources between industrial processes and nearby urban areas to increase efficiency and reduce waste. Combining the concepts of PED and UIS can create self-sufficient, sustainable, and resilient districts. As the analysis and implementation of such systems are barely studied in North America, this research study was structured to fill the gap by evaluating the financial and environmental advantages of this combination. This study proposes a methodology to design a heat transmission system; then, it is applied to the case of a paper-making factory and a multifunctional heritage building in Montreal, Canada. The results show that the building’s new heating system can generate sufficient heat while emitting near-zero direct emissions. Overall, this paper argues that combining the concepts of PED and UIS can lead to a more sustainable and resilient urban area, and provides a roadmap for achieving this goal.

Abstract Pinch analysis and Mixed Integer Linear Programming (MILP) have been extensively studied for optimization of industrial processes addressing heat recovery, utility selection and sizing. Analysis of renewable utility integration, such as solar thermal or photovoltaics, introduces several obstacles for established methods: the time-dependency of resources, storage inertia and losses, and intrinsic non-linearities of the system performance are difficult to represent by linearized, time-invariant MILP equations. Moreover, waste heat recovery options such as heat pumping cannot be neglected as a potential competitor to solar heat. This work presents a set of multi-period MILP equations for solar technologies as well as a superstructure for optimization of heat pump cycles. Additionally, a methodology is proposed and applied to simultaneously optimize the process' refrigeration and renewable utility system using ɛ-constrained parametric optimization. The proposed methodology is illustrated on the basis of a dairy plant for which the different utility technologies are compared and evaluated based on economic and environmental criteria. It is illustrated that integration of solar energy can contribute to strongly reduce the environmental impact of the process (65–75% reduction in CO2 equivalent emissions), but only in combination with heat recovery (27%) and an improved heat pump system (33%). Heat recovery and heat pump placement for industrial processes are hereby shown to reduce exergy destruction and total cost while improving system energy efficiency by means of thermo-economic optimization. The solutions show that investment in solar energy can be economically and environmentally attractive for industrial processes by considering the whole system and ensuring that solar energy is optimally integrated and utilized.

Eco-industrial networks are a concept which has received study for almost 30 years as a strategy for improving economics of production facilities while reducing waste. This study focuses on developing a generalized modeling framework in terms of mixed-integer nonlinear programming of an eco-industrial network. The problem is formulated as a multi-objective optimization with the objective to reduce life-cycle emissions and maintaining favourable economics. This model lends itself as a developmental tool to assess environmental and economic feasibility of applying eco-park concepts to new construction projects. In addition, the model is formulated to determine feasible transportation distances between facilities, select the appropriate transportation technologies and is built with the intention of modification toward stochastic import/export pricing. The sensitivity of the network configuration to variances in the weighting factors placed on economics or environmental emissions is discussed and the results from multiple weighting scenarios are presented. These results show that the estimated reductions in cost and emissions are less significant than those from previously published work but are founded upon a more realistic network model. Based on our findings, the cost for the integrated set of facilities is shown to be reduced by 24% while the emission reduction is observed at 12.7% for the base scenario examined by the earlier models. These results show less promise than the earlier findings but represent a more realistic assessment of the proposed network. The tools developed by this research present a novel approach to facility planning and policy development for chemical industries. As such, the framework presented herein has the potential to impact facility construction/operation and public policy governing industrial producers; additionally, this research contributes an empirical approach for assessing the practicality of establishing eco-industrial networks and provides an approach for further analyses.

The residential sector accounts for a large share of worldwide energy consumption, yet is difficult to characterise, since consumption profiles depend on several factors from geographical location to individual building occupant behaviour. Given this difficulty, the fact that energy used in this sector is primarily derived from fossil fuels and the latest energy policies around the world (e.g., Europe 20-20-20), a method able to systematically integrate multi-energy networks and low carbon resources in urban systems is clearly required. This work proposes such a method, which uses process integration techniques and mixed integer linear programming to optimise energy systems at both the individual building and district levels. Parametric optimisation is applied as a systematic way to generate interesting solutions for all budgets (i.e., investment cost limits) and two approaches to temporal data treatment are evaluated: monthly average and hourly typical day resolution. The city center of Geneva is used as a first case study to compare the time resolutions and results highlight that implicit peak shaving occurs when data are reduced to monthly averages. Consequently, solutions reveal lower operating costs and higher self-sufficiency scenarios compared to using a finer resolution but with similar relative cost contributions. Therefore, monthly resolution is used for the second case study, the whole canton of Geneva, in the interest of reducing the data processing and computation time as a primary objective of the study is to discover the main cost contributors. The canton is used as a case study to analyse the penetration of low temperature, CO2-based, advanced fourth generation district energy networks with population density. The results reveal that only areas with a piping cost lower than 21.5 k€/100 m2ERA connect to the low-temperature network in the intermediate scenarios, while all areas must connect to achieve the minimum operating cost result. Parallel coordinates are employed to better visualise the key performance indicators at canton and commune level together with the breakdown of energy (electricity and natural gas) imports/exports and investment cost to highlight the main contributors.