Carbon Management Networks Research Articles

The P-graph approach in optimal synthesis and planning of waste management towards achieving sustainable development goals: A systematic review.

Effective waste management remains a challenge in global environmental sustainability, underlining the urgent necessity for innovative solutions. This review explored waste management strategies, focusing on the role of P-graph frameworks in achieving sustainable development goals (SDGs). P-graphs offer a systematic approach across domains including, chemical reaction routes, carbon management networks, economic systems and resource planning to waste management synthesis and planning. Through a systematic search and analysis of relevant P-graph approaches, 28 articles meeting the inclusion criteria were selected for review. The study reveals that P-graph approach is a systematic methodology that can streamline decision-making processes, which ultimately lead to more efficient and effective waste management strategies and solutions. This research also highlighted the absence of previous studies on the application of the P-graph approach to various types of waste, underscoring its significance and originality in the field. This study seeks to advance the achievement of SDGs and promote sustainable waste management practices through the integration of the P-graph framework with waste management solutions.

Hydrogen infrastructure modeling in macro-energy systems models - Lessons learned, best practices, and potential next steps (workshop insights)

This short communication is based on a workshop on hydrogen network modeling in macro-energy system models discussing the whole hydrogen value chain: production, transmission, storage, and use, as well as the related issues of demand flexibility, alternative fuels of biological origin, and the integration with district heating. It was organized by the Danish research network ENERforsk with leading modelers from academia, industry, and transmission system operators. The workshop collected (1) lessons learned, (2) best practices, and (3) potential next steps. We conclude that no-regret pathways need to consider evolving hydrogen regulation while balancing social aspects with interests driven by green industrial policy. Important and uncertain issues include the repurposing of natural gas networks, considering hydrogen pipeline standards and existing contracts, or the disposal of brine water. As more demand sectors and carbonaceous fuels are included in the models, carbon management, sustainable biomass, and carbon networks become more important. Collecting and incorporating lessons learned, best practices, and potential next steps will help the modeling community and policymakers to develop sound policies.

Pinch-based planning of terrestrial carbon management networks

Negative emissions technologies (NETs) will be needed to achieve deep cuts in greenhouse gas emissions and mitigate climate change in the coming decades. Enhanced weathering and biochar application are two NETs that are based on the enhancement of naturally occurring processes; compared to other NETs, they have the advantage of relying on mature component technologies. Both technologies offer the prospect of sequestering carbon from the atmosphere at the scale of multiple gigatons per year if deployed commercially via carbon management networks (CMNs). However, planning future CMNs will present similar challenges as optimizing supply chains for current product systems. In this work, a pinch analysis approach for the planning of CMNs based on either enhanced weathering or biochar application is developed. Planning is framed as a source-sink problem typical in process integration (PI) applications, allowing pinch analysis to be applied to determine optimal system targets and synthesize the corresponding CMNs. General principles of pinch analysis are shown to be applicable to the planning problem. Case studies on enhanced weathering and biochar application CMNs are solved to illustrate this approach.

Regional carbon drawdown with enhanced weathering of non-hazardous industrial wastes

Countries around the world are taking active efforts to address the current climate crisis. Negative emissions technologies (NET) such as enhanced weathering will play a significant role in these efforts in the coming decades. Enhanced weathering is based on the terrestrial application of powdered natural or synthetic silicate minerals to remove carbon dioxide (CO2) from the atmosphere. In the latter case, non-hazardous industrial waste (NHIW) can react with water and CO2 from the atmosphere to achieve carbon drawdown through the acceleration of the naturally occurring weathering process. This approach also addresses the problem of sustainable industrial waste management. Besides, carbon drawdown also enhances soil quality for agricultural application sites. In the future, planning the large-scale implementation of enhanced weathering via carbon management networks can be facilitated by using optimization models. In this work, a source-sink model is developed for the proper allocation of NHIW to application sites. The method is demonstrated with a case study based at Shandong Province, China. Two scenarios are analyzed, with both achieving significant negative CO2 emissions. The CO2 sequestration rates for these two scenarios are determined as 12.73 Mt/y and 13.77 Mt/y, respectively. Scenario 1 shows that the system can achieve negative CO2 emissions at a cost of $ 184.25/t CO2, which is comparable to previously reported cost estimates. Given the necessary input data, the model can be used to generate case-specific estimates for any country. The increasing use of clean energy in the transport and crushing process will enhance CO2 sequestration of the system.

A fuzzy optimization model for planning integrated terrestrial carbon management networks

A fuzzy optimization model for planning integrated terrestrial carbon management networks

An Optimization Framework for Biochar-based Carbon Management Networks

Biochar-based carbon management networks (BCMNs) are systems that are intended to strategically plan carbon sequestration via systematic production and allocation of biochars for long-term storage to agricultural lands and for simultaneous improvement of soil properties. Other significant potential benefit includes supply of clean energy in gaseous (biogas) or liquid (bio-oil) form. However, a challenge still exists in determining the levels of biochar contaminants that the soil can tolerate. This risk implies that adequate planning will be needed to ascertain the suitability of sinks to biochar application in order to lessen the potential for adverse effects on soil. To maximize the potential benefits of biochar, a modeling framework for BCMNs can be developed with the aid of Process Systems Engineering (PSE) techniques so as to ensure that the quality requirements set for the sinks are met. To fill this research gap in the global biochar literature, this work develops an optimization framework for BCMNs by accounting for the relevant and practical aspects of biochar research. To demonstrate the applicability of the framework, a representative network is explored, which attains a profit of USD 17,453,810 and a cumulative CO2 sequestration of 876,961.4 t. The results show how the framework can be used to support decision-making for the proper deployment of BCMNs.

Optimisation of biochar-based supply chains for negative emissions and resource savings in carbon management networks

Climate change is a global issue, and the main reason causing climate change is carbon dioxide (CO2) emissions. Biochar, a stable form of carbon that can be obtained from pyrolysis of biomass, can potentially reduce CO2 emissions. By applying biochar into soil, a stable form of carbon is stored under the soil. Biochar application is able to achieve negative emissions because the stored carbon was previously fixed from CO2 via photosynthesis. Biochar application also brings additional benefits to soil as it helps to retain water and nutrients in soil. The benefits of biochar can be scaled up by developing biochar-based carbon management networks (CMNs). However, there are very limited decision support tools available for designing optimal biochar-based CMNs. This work presents a methodology to develop a decision support tool for biochar-based CMNs. Proposed methodology provides guidance on how a mixed-integer linear mathematical model can be formulated as a decision support tool to design biochar-based CMNs. The developed model has capabilities to determine the optimal network based on cost while considering several constraints. These constraints are CO2 emission reduction, water and nutrient retention and several land constraints. The network costs considered are capital cost, operating cost, transportation cost and transport fuel consumption cost. The model also factors transport routes, hiring cost and truck capacity into the network. A case study based on the palm oil industry is solved to demonstrate the use of the model. Results from the case study show that total biochar production, water savings, nutrient savings, cost savings and total network costs were 307 t/day, 7.1 M m3 water/year, 6.0 k t nutrients/year, RM 25.6 M/year and RM 17.9 M/year, respectively. Besides that, a sensitivity analysis is done to analyse the impact of CO2 emission reduction targets on the network costs. Aside from this, two alternative scenarios from the case study were investigated further.

Process Integration and Climate Change: From Carbon Emissions Pinch Analysis to Carbon Management Networks

Carbon Emissions Pinch Analysis (CEPA) is a branch of Process Integration (PI) that was developed as a systematic methodology for planning the optimal allocation of energy to various demands under carbon-constrained conditions. Since its inception in 2007, the body of CEPA literature has branched out into distinct areas. The first branch consists of direct applications of CEPA to specific geographic regions or nations, which includes early applications in Ireland and New Zealand, leading to more recent applications to parts of China and the United States. The second branch consists of methodological extensions of the original graphical CEPA procedure. These developments include algebraic and Mathematical Programming (MP) variants. The third branch of CEPA literature extends its principles to other measures of sustainability, such as various environmental footprints; recent attempts to allow the methodology to simultaneously handle multiple sustainability metrics have also been published. Finally, in the fourth branch, CEPA has also been extended to handle special problem structures such as segregated targeting for multiple geographic zones, or deployment of CO2 capture and storage (CCS). Also included here are CEPA variants for carbon-constrained planning at different levels, ranging from enterprise scale, to supply chains and sector level (e.g., transportation or waste disposal), and finally to economy-wide analysis when integrated with established tools such as Input-Output Analysis (IOA). This paper discusses key developments in CEPA literature, with emphasis on the most recent developments (2016 to the present), as well as further prospects for the development of this PI sub-area.

Fuzzy optimization of carbon management networks based on direct and indirect biomass co-firing

A drastic reduction in greenhouse gas emissions from electricity generation will be needed to mitigate climate change to a safe level. Residual biomass from agriculture is an underutilized energy source that can contribute to the needed emissions cut, but its geographic dispersion presents logistical problems. Direct and indirect co-firing of biomass in existing power plants presents a flexible means of utilizing this resource. Indirect co-firing of biomass with biochar co-production can even give greater reduction in greenhouse gas emissions if the biochar is applied to soil as a form of carbon sequestration. In this paper, a fuzzy linear programming model is developed for optimizing a carbon management network based on direct and indirect biomass co-firing, coupled with biochar application to soil for the latter case. The model can match biomass sources to power plants; the power plants that use indirect co-firing are also matched to biochar application sites. The model is illustrated using a case study representative of a developing country with an agriculture-intensive economy. Results show that not all powerplants need to implement co-firing to reach a balance between reducing GHG emissions and the risk of introducing contaminants in soil. The model provides effective decision support for decarbonizing power generation, particularly in developing countries that still make use of coal-fired power plants and which have abundant biomass resources in the form of agricultural waste.

A hyperbox classifier model for identifying secure carbon dioxide reservoirs

Carbon management technologies such as carbon dioxide capture and storage and direct air capture systems will be needed to mitigate climate change in the coming decades. Both of these technologies will depend on the availability of secure geological storage sites that can permanently hold carbon dioxide with minimal risk of leakage. Machine learning tools that can characterize candidate storage sites based on geological data can aid decision-makers in planning carbon management networks. In this work, a mixed integer linear programming model is developed to generate a binary hyperbox classifier for determining the integrity of a candidate storage site. The model is calibrated and validated using literature data on natural carbon dioxide reservoirs, resulting in a set of IF-THEN rules that are readily interpreted by decision-makers. The approach developed here also includes rule simplification features and the capability to account for statistical Type I (false positive) and Type II (false negative) errors. Different sets of rules can be generated using the model based on user-defined number of hyperboxes. The best set of rules can be selected based on a combination of its performance with the validation data and consistency with expert knowledge. Using the case study for identifying secure CO2 reservoirs, the set of rules which resulted in zero false positives using the validation data was generated using three hyperboxes. However, an alternative set of rules which falsely predicted two out of three insecure sites as positive provides simpler rules indicating CO2 density and reservoir depth as the most important criteria.

Optimization-based decision support methodology for the synthesis of negative-emissions biochar systems

Biochar-based carbon management networks (BCMNs) can be an effective climate change mitigation strategy. Such systems have the potential to sequester stable carbon in biochar through application to soil, thus achieving negative emissions through the net transfer of carbon from the atmosphere into the ground. There may also be secondary co-benefits that result from the complex interactions of the biochar with the receiving soil. In order to maximize system-wide benefits, process systems engineering (PSE) tools can be used to optimize integrated BCMNs so as to customize biochars to specific soil quality limitations. This research gap is addressed in this work. A model is developed to optimize BCMNs where the syngas coproduced with biochar is utilized as fuel for the polygeneration system. The BCMN can achieve negative carbon footprint due to sequestration of carbon in the biochar coupled with displacement of fossil energy-intensive products. Since both optimal and near-optimal are needed for more effective decision-making, a mixed integer linear programming model (MILP) with integer-cut constraints is developed for the synthesis of polygeneration systems with biochar production. Integer cuts of the MILP model can be solved to generate a ranked set of near-optimal solutions alongside the optimal solution; these alternative solutions can then be analyzed during process synthesis. Two case studies are used to demonstrate how the methodology can generate insights for better decision-making, such as revealing patterns of features that repeatedly occur in good solutions. These insights enable practical constraints arising in industrial settings to be accounted for.

Bi-Objective Mixed-Integer Linear Programming Model for High-Level Planning of Biochar-Based Carbon Management Networks

Biochar is the stable, carbon-rich solid co-product of thermochemical biomass conversion. It has recently gained considerable interest, driven by the need to mitigate climate change through carbon sequestration in soil. During pyrolysis, much of the carbon in biomass is transformed into recalcitrant form, so that biochar applied to soils results in storage of carbon for hundreds of years and simultaneous improvement of soil fertility. Biochar can display a wide range of properties such as pH, cation exchange capacity (CEC) and elemental composition due to the assortment of raw materials and reaction conditions. The suitability of soil to biochar application depends on these properties. This has led to the “designer biochar” concept, wherein biochar could be tailored with relevant properties to address specific soil quality improvements. This aspect and the promising results of biochar application to soil can be potentially optimized through biochar-based carbon management networks (CMN) with the aid of mathematical programming. In this work, a bi-objective mixed-integer linear programming (MILP) model is developed for the high-level planning of biochar-based CMN. Additional parameters, variables and constraints are given to account for the incompatibilities of biochar sources and sinks. An illustrative case study is presented here to demonstrate the applicability of the developed model.

Synthesis of optimal and near-optimal biochar-based Carbon Management Networks with P-graph

The application of biochar to soil is a potentially significant way to achieve negative net emissions. Photosynthesis fixes carbon from the atmosphere during plant growth; subsequently, pyrolysis stabilizes the carbon in biomass into recalcitrant form, which results in long-term storage when the carbonized product is put in soil. However, effective planning of such Biochar-based Carbon Management Networks is needed to ensure that benefits are maximized, and that adverse consequences are mitigated. The objective of this work is to develop a P-graph methodology for planning Biochar-based Carbon Management Networks, where pyrolysis plants act as sources, while the agricultural lands that receive the biochar act as sinks. Two problem variants are considered. In the first case study, the allocation of biochar is constrained by the presence of contaminants that exist naturally in the biomass or are formed during pyrolysis, such that only 72.5% of the available biochar is applied to soil. In the second case study, the distribution of biochar is limited both by the ultimate soil carbon limit, as well as annual application rates at each site; 88.9% of the available biochar is used in the optimal solution. The P-graph framework also generates near-optimal network topologies, which present alternative solutions that can be useful for the large-scale implementation of Biochar-based Carbon Management Networks.

Bi-objective optimization of biochar-based carbon management networks

Abstract Biochar is the carbon-rich solid product derived from thermochemical processing of biomass. Its application to soil can sequester atmospheric carbon, leading to negative net emissions, while also improving agricultural yield. Biochar-based carbon management networks thus have the potential for scalable contributions to climate change mitigation efforts. However, it is necessary to allocate biochar of suitable quality to appropriate sinks, based on contaminant tolerance limits of soil. In this work, the network is modelled as a bi-objective mixed integer linear programming model, with profit and carbon sequestration as the objective functions. The model calculates the optimum allocation of biochar such that their prescribed limits for the contaminants are met. A Philippine case study is solved under different scenarios to illustrate the practical use of the developed model. The results provide useful insights for the rational decision-maker to arrive at the most preferred solution among the Pareto optimal choices. Comparison of the two scenarios substantiate the significance of unique sequestration factor as an additional parameter in the model to account for the interactions between the soils and biochars so that the maximum sustainable potential of biochar in climate change mitigation can be properly assessed.

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

A P-graph and Monte Carlo simulation approach to planning carbon management networks is proposed. These networks are generalized systems for minimizing emissions of CO2. 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 CO2 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.