Modular Supply Network Optimization of Renewable Ammonia and Methanol Co-production

To reduce the use of fossil fuels and other carbonaceous fuels, renewable energy sources such as solar, wind, geothermal energy have been suggested to be promising alternative energy that guarantee sustainable and clean environment. However, the availability of renewable energy has been limited due to its dependence on weather and geographical location. This challenge is intended to be solved by the utilization of the renewable energy in the production of chemical energy carriers. Hydrogen has been proposed as a potential renewable energy carrier, however, its chemical instability and high liquefaction energy makes researchers seek for other alternative energy carriers. Ammonia and methanol can serve as promising alternative energy carriers due to their chemical stability at room temperature, low liquefaction energy, high energy value. The co-production of these high energy dense energy carriers offers economic and environmental advantages since their synthesis involve the direct utilization of CO2 and common unit operations. This problem report aims to review the optimization of the co-production of methanol and ammonia from renewable energy. Form this review, research challenges and opportunities are identified in the following areas: (i) optimization of methanol and ammonia co-production under renewable and demand uncertainty, (ii) impacts of the modular exponent on the feasibility of co-production of ammonia and methanol, and (iii) development of modern computational tools for systems-based analysis

Life cycle assessment (LCA) applied to the process industry: a review

Purpose : Life cycle assessment (LCA) methodology is a well-established analytical method to quantify environmental impacts, which has been mainly applied to products. However, recent literature would suggest that it has also the potential as an analysis and design tool for processes, and stresses that one of the biggest challenges of this decade in the field of process systems engineering (PSE) is the development of tools for environmental considerations. Method : This article attempts to give an overview of the integration of LCA methodology in the context of industrial ecology, and focuses on the use of this methodology for environmental considerations concerning process design and optimization. Results : The review identifies that LCA is often used as a multi-objective optimization of processes: practitioners use LCA to obtain the inventory and inject the results into the optimization model. It also shows that most of the LCA studies undertaken on process analysis consider the unit processes as black boxes and build the inventory analysis on fixed operating conditions. Conclusions : The article highlights the interest to better assimilate PSE tools with LCA methodology, in order to produce a more detailed analysis. This will allow optimizing the influence of process operating conditions on environmental impacts and including detailed environmental results into process industry

Machine Learning-Based Data and Model Driven Bayesian Uncertanity Quantification of Inverse Problems for Suspended Non-structural System

Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and control strategies based on simulation or prediction results. However, in the surrogate model, preventing overfitting and incorporating reasonable prior knowledge of embedded physics and models is a challenge. Suspended Nonstructural Systems (SNS) pose a significant challenge in the inverse problem. Research on their seismic performance and mechanical models, particularly in the inverse problem and uncertainty quantification, is still lacking. To address this, the author conducts full-scale shaking table dynamic experiments and monotonic & cyclic tests, and simulations of different types of SNS to investigate mechanical behaviors. To quantify the uncertainty of the inverse problem, the author proposes a new framework that adopts machine learning-based data and model driven stochastic Gaussian process model calibration to quantify the uncertainty via a new black box variational inference that accounts for geometric complexity measure, Minimum Description length (MDL), through Bayesian inference. It is validated in the SNS and yields optimal generalizability and computational scalability

Improving the determination of soil hydraulic properties of peat soils at different scales

This thesis improves the characterization of unsaturated hydraulic properties for different types of peat and other organic soils at the laboratory and the field scale. First, suggestions for a general improvement of the unsaturated hydrological modeling in peatlands are made. Second, a novel one-dimensional expression for calculating specific yield for shallow groundwater systems with microrelief has been derived which was the basis for the development of a novel in situ method for determining soil water retention characteristics in shallow groundwater systems

Towards a representation of priming on soil carbon decomposition in the global land biosphere model ORCHIDEE (version 1.9.5.2)

Priming of soil carbon decomposition encompasses different processes through which the decomposition of native (already present) soil organic matter is amplified through the addition of new organic matter, with new inputs typically being more labile than the native soil organic matter. Evidence for priming comes from laboratory and field experiments, but to date there is no estimate of its impact at global scale and under the current anthropogenic perturbation of the carbon cycle. Current soil carbon decomposition models do not include priming mechanisms, thereby introducing uncertainty when extrapolating short-term local observations to ecosystem and regional to global scale. In this study we present a simple conceptual model of decomposition priming, called PRIM, able to reproduce laboratory (incubation) and field (litter manipulation) priming experiments. Parameters for this model were first optimized against data from 20 soil incubation experiments using a Bayesian framework. The optimized parameter values were evaluated against another set of soil incubation data independent from the ones used for calibration and the PRIM model reproduced the soil incubations data better than the original, CENTURY-type soil decomposition model, whose decomposition equations are based only on first-order kinetics. We then compared the PRIM model and the standard first-order decay model incorporated into the global land biosphere model ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems). A test of both models was performed at ecosystem scale using litter manipulation experiments from five sites. Although both versions were equally able to reproduce observed decay rates of litter, only ORCHIDEE-PRIM could simulate the observed priming (R² = 0.54)in cases where litter was added or removed. This result suggests that a conceptually simple and numerically tractable representation of priming adapted to global models is able to capture the sign and magnitude of the priming of litter and soil organic matter

강화된 친환경 화학 공정의 타당성 개선을 위한 시뮬레이션 기반 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부(에너지환경 화학융합기술전공), 2021.8. 이종민.Technologies to mitigate risks from climate change have made significant advances in both research academia and industry. However, most advanced techniques were still considered only in the lab-scale experience. Moreover, the process intensification development during the process synthesis is in its incipient stages. To promote the application of a novel process technique to an eco-friendly process, the process feasibility of economy or operation should be considered. In this thesis, simulation-based framework to improve feasibility of intensified chemical processes, which are suggested under limited experimental conditions, is proposed. To solve the feasibility problem derived from the characteristics of process intensification, which is mainly developed by the numerous experiments rather than derived from the theoretical verification, the digital twin technology, which is developed to simulate various situations by modeling the reactor and process, is implemented. In addition, the framework, including the procedures from verification or validation of developed digital twin model to feasibility study and improvement of economic and operational feasibility, is proposed. First, intensified eco-friendly processes such as the biodiesel production process and carbon capture and utilization process were simulated, and comparative study and optimization were conducted to improve the economic or operational feasibility of that processes. As an example of applying the procedure to verify and to improve the economic feasibility, the economic feasibility study on the intensified biodiesel production process is implemented to increase the profitability of the biodiesel production process by reducing the process units, enhancing biodiesel quality, and reducing the raw material cost. As an example of applying the procedure to verify and to improve the operational feasibility, the modeling and validation of the semi-continuous carbonation process are implemented to estimate the overall CO2 removal efficiency during the operation and when the reaction ends. Using the developed process model, the operational feasibility of the semi-continuous carbonation process is verified and the optimization algorithms is adopted to obtain the optimal operation recipes. For the effective operational feasibility improvement, two new operation recipes were suggested and optimized via Bayesian optimization. Consequently, in order to verify and improve the applicability of the newly proposed intensified process, a methodology is proposed including the process modeling, which is conducted using laboratory-scale experimental data for the reaction kinetic studies, economic analysis, sensitivity analysis, and comparative study. In addition, the process modeling and optimization, using pilot-scale operation data, are carried out. Especially, the operational feasibility of semi-continuous carbonation process is effectively improved by proposing new operation recipe as well as adopting the digital twin model to the Black-box optimization method. In this thesis, a framework, improving economic/operational feasibility of newly proposed intensified chemical processes with two different experimental data depending on the purpose, is developed.기후 변화로 인한 위험들을 완화하는 기술들은 학술 분야와 산업 모두에서 상당한 발전을 이루었다. 그러나 대부분의 진보된 기술들은 여전히 실험실 규모의 실험에서만 고려되고 있다. 더욱이, 공정 합성 과정에서의 공정 강화 개발은 초기 단계에 머물러있다. 새로운 공정 기술들을 친환경 공정에 적용하는 것을 촉진시키기 위해서는, 공정의 경제성 또는 운전 타당성을 고려해야 한다. 본 학위논문에서는 제한된 실험 조건 하에서 제안된, 강화된 친환경 공정의 시뮬레이션 기반 타당성 개선 방법론을 제시하였다. 이론적인 검증을 통한 공정 강화가 아닌 수많은 실험을 통해 제안되는 공정 강화의 방식에서 파생되는 타당성 검증에 대한 문제를 해결하고자, 공정 및 반응기의 모델을 구축하고 다양한 상황을 모사하는 디지털 트윈 기술을 구현하였다. 또한, 구축된 디지털 트윈 모델의 검증부터, 강화된 공정의 경제적 타당성 및 운전 타당성 검증 및 개선까지의 일련의 과정들을 포함하는 방법론을 제시하였다. 먼저, 바이오디젤 생산 공정과 탄소 포집 및 활용 공정과 같은 강화된 친환경 공정을 시뮬레이션하고, 해당 공정의 경제적 타당성 또는 운전 타당성을 개선하기 위해 비교 연구 및 최적화를 수행하였다. 경제적 타당성 검증 방법론을 적용한 한가지 예제로써, 공정 유닛을 줄이고, 바이오디젤 품질 향상 및 원재료 비용을 감소시킴으로써 바이오디젤 생산 공정의 수익성을 증가시키는 강화된 바이오디젤 생산 공정의 경제적 타당성 연구를 수행하였다. 운전 타당성 검증 방법론을 적용한 한가지 예제로써, 반연속식 탄산화 공정의 운전 중 전체 이산화탄소 제거 효율과 반응 종료 시기를 추정하기 위해 시뮬레이션 기반 모델링 및 구축된 모델의 검증을 수행하였다. 구축된 시뮬레이션 기반 모델을 이용하여, 반연속식 탄산화 공정의 운전 타당성을 확인하고 최적의 운전 레시피를 얻기 위해 최적화 알고리즘에 적용하였다. 효과적인 반연속식 탄산화 공정의 운영 타당성 개선을 위해 두개의 새로운 운전 레시피를 제시하였고, 베이지안 최적화 기법을 이용하여 최적화하였다. 결론적으로, 반응식 및 반응 계수 연구를 위한 실험실 규모의 실험 데이터를 활용하여 공정 모델을 구축하고, 경제성 분석, 민감도 분석, 비교 분석을 통해 새로 제안된 강화된 공정의 현업에의 적용 가능성을 검증하고 개선시키는 방법론을 제안하였다. 또한, 실제 현업에 적용되는 크기의 반응기를 가진 중간 규모의 실험 데이터를 활용하여 공정 모델을 구축하고 최적화하였다. 특히, 반연속식 공정의 새로운 운전 방식을 제안함과 동시에 구축된 디지털 트윈 모델에 블랙박스 (Black-box) 최적화 기법을 적용하여 제안된 반연속식 친환경 공정의 운전 타당성을 효과적으로 개선할 수 있었다. 본 학위논문에서는 목적에 따라 다른 두 가지의 실험 결과를 이용하여, 새롭게 제안된 강화된 친환경 공정의 경제적 또는 운전 타당성 검증 및 개선을 위한 방법론을 제시하였다.Chapter 1 1 Introduction 1 1.1 Research motivation 1 1.2 Target process descriptions 4 1.2.1 Biodiesel production process 4 1.2.2 Aqueous mineral carbonation process 6 1.3 Outline of the thesis 8 1.4 Associated publications 8 Chapter 2 9 Economic feasibility study on biodiesel production process 9 2.1 Introduction 9 2.2 Reaction kinetics 13 2.3 Process simulation 15 2.3.1 Material and thermodynamic model 18 2.3.2 Assumptions 22 2.3.3 SC PFR 22 2.3.4 Cu-based PBR 26 2.3.5 Pd-based PBR 30 2.4 Economic analysis 33 2.4.1 Total capital investment 37 2.4.2 Total manufacturing cost 39 2.4.3 Sensitivity analysis 41 2.5 Summary 46 Chapter 3 48 Modeling and validation of pilot-scale aqueous mineral carbonation process 48 3.1 Introduction 48 3.2 Reaction kinetics 48 3.2.1 Calcium hydroxide dissolution in water 49 3.2.2 Mass transfer of CO2 gas into the alkali solution 50 3.2.3 Ionic reactions and precipitation of calcium carbonate 53 3.3 Process design and modeling 56 3.3.1 Assumptions 56 3.3.2 Reactor modelling 57 3.3.3 Sequence of reactant replenishment 59 3.3.4 ACM model validation 63 3.4 Summary 65 Chapter 4 66 Bayesian optimization approach to semi-continuous carbonation process operation recipe 66 4.1 Introduction 66 4.2 Problem descriptions 68 4.3 Multi-objective Bayesian optimization algorithm 76 4.4 Results and discussion 84 4.5 Summary 91 Chapter 5 Concluding remarks 94 References 97 Abstract in Korean (국문초록) 106박

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools