Biorefineries can reduce carbon dioxide emissions while serving the global chemical demand market. Governments are also using carbon pricing policies, such as carbon taxes, cap-and-trade models, and carbon caps, as a strategy to reduce emissions. The use of biomass feedstocks in conjunction with carbon capture usage and storage technologies are mitigation strategies for global warming. Businesses can invest in these technologies to accommodate the adoption of these policies. Rapid action is necessary to halt global warming, which results in aggressive policies. In this work, a multi-period process design and planning problem is developed for the design and capacity expansion of biorefineries. The three carbon pricing policies are integrated into the model and parameters are selected according to the aggressive scenario denoted by the Paris Agreement. The results show that the cap-and-trade policy achieves a higher net present value evaluation over the carbon tax model across all pareto points due to the flexibility of the allowances in the cap-and-trade policy. The carbon cap model substantial investments are required in carbon capture technologies to adhere to the emissions constraints.

Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportunities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially concerning chemical reagent usage. Feed concentrations and product demand variability require flexible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, thus informing materials optimization with process and infrastructure scale performance targets. Finally, scale-up must be accelerated and de-risked across the materials-to-process levels to fully realize the opportunity presented by membranes, thereby fostering the development of a circular economy for critical minerals. Tackling these challenges requires integrating efforts across diverse disciplines. We advocate for a holistic molecular-to-systems perspective for fully realizing PI with membranes to address sustainability challenges in critical mineral processing. The opportunities for PI with membranes are excellent applications for emerging research in machine learning, data science, automation, and optimization.

In this work, we propose a Mixed Integer Nonlinear Programming (MINLP) model to determine the optimal sustainable design of a poly(hydroxyalkanoate)s (PHAs) production plant configuration and its heat exchanger network (HEN). The superstructure-based optimization model considers different carbon sources as raw material: glycerol (crude and purified), corn starch, cassava starch, sugarcane sucrose and sugarcane molasses. The PHA extraction section includes four alternatives: the use of enzymes, solvent, surfactant-NaOCl or surfactant-chelate. Model constraints include detailed capital cost for equipment, mass and energy balances, product specifications and operating bounds on process units. To assess the feasibility of the PHA plant, we considered the Sustainability Net Present Value (SNPV) as the objective function, a multi-criteria sustainability metric that considers economic, environmental and social pillars. The Net Present Value (NPV) was also calculated. SNPV metric provides useful insights on sustainable PHA production, as the optimal technological route results in the sugarcane-surfactant-chelate option, rather than the sugarcane-enzyme pathway which proves more economically profitable, but with higher environmental impacts. Moreover, inclusion of HEN design significantly improves the objective function value, mainly due to a 24% carbon footprint impact reduction.

Hydrogen, as a clean and versatile energy carrier, holds immense promise for addressing the world�s growing energy and environmental challenges. However, hydrogen-based energy systems face challenges related to efficient storage methods, energy-intensive production, refueling processes, and overall cost-effectiveness. To solve this problem, a superstructure was developed that integrates overall technologies related to hydrogen energy transportation. This study synthesizes process pathways for hydrogen energy transportation method including energy carrier production, storage, and refueling, based on the developed superstructure. The techno-economic analysis was conducted to evaluate the performance of each transportation pathway and compare it with conventional fossil fuel transportation system. Process performance criteria, including unit production cost (UPC), energy efficiency (EEF), and net CO2 equivalent emissions (NCE), serve as indicators for process performance. By comparing technological pathways, we can propose the most economically and environmentally optimal energy refueling route. Additionally, sensitivity analyses were performed on various external factors, identifying influential variables in the decision-making process for hydrogen production, storage, and refueling strategies, while also elucidating technological limitations.

The food-energy-water nexus (FEWN) has been receiving increasing interest in the open literature as a framework to address the widening gap between natural resource availability and demand, towards more sustainable and cost-competitive solutions. The FEWN aims at holistically integrating the three interconnected subsystems of food, energy and water, into a single representative network. However, such an integration poses formidable challenges due to the complexity and multi-scale nature of the three subsystems and their respective interconnections. Additionally, the significant input data uncertainty and variability, such as energy prices and demands, or the evaluation of emerging technologies, contribute to the system�s inherent complexity. In this work, we revisit the FEWN problem in an attempt to elucidate and address in a systematic way issues related to its multi-scale complexity, uncertainty and variability. In particular, we provide a classification of the sources of data and technology uncertainty from historic data, forecasting and process parameters, and propose ways to quantify their impact on the integrated system analysis. To effectively tame the FEWN�s multi-scale complexity, we distinguish between the introduced error of approximation and optimization of employed surrogate models. In turn, it is possible to characterize their impact on optimal FEWN decision-making based on the quantification of the introduced errors at all levels. Thus, we present strategies to systematically characterize FEWN process systems modeling and optimization. Ultimately, this facilitates translating obtained solutions into actionable knowledge by quantifying the level of confidence one can have in the derived process model and optimal results.

The synthesis of energy systems necessitates simultaneous optimization of both design and operation across all components within the energy system. In real-world applications, this synthesis poses a mixed-integer nonlinear programming (MINLP) problem, considering nonlinear behaviours such as investment cost curves and part-load performance. The complexity increases further when seasonal energy storage is involved, as it requires temporal coupling of the full time series. Although numerous solution approaches exist to solve the synthesis problems simplified by linearization, methods for solving a full-scale problem are currently missing. In this work, we introduce a rigorous method, RiNSES4, to manage the nonlinear aspects of energy system synthesis, particularly focusing on long-term time-coupling constraints. RiNSES4 calculates the upper and lower bounds of the initial synthesis problem in two separate branches. The proposed method yields feasible solutions through upper bounds, while evaluating the solution quality via lower bounds. The solution quality is iteratively enhanced by increasing the resolution for calculating upper bounds and tightening the relaxations for computing lower bounds. Both branches work simultaneously and independently, with their outcomes compared after each iteration within each branch. The iterations continue until a predefined optimality gap is reached. We apply RiNSES4 to design a photovoltaic and battery energy system, considering the seasonality of both energy supply and demand sides. In comparison with a state-of-the-art commercial solver, RiNSES4 enables to solve the MINLP synthesis problem with great temporal detail and shows high potential.

Maintaining product integrity in multi-product oil pipelines is crucial for efficiency and profit. This study presents a strategy combining design and process improvement to enhance flushing protocols, addressing the challenge of residual batch contamination. A pilot plant, mirroring industrial operations through dimensionless residence time distribution, was developed to identify and rectify bottlenecks during product transition. The pilot plant�s success in replicating industrial operations paves the way for targeted experiments and modelling to enhance optimized flushing, ensuring product quality and operational excellence.

Sustainable and economically viable plastic recycling methodologies are vital for addressing the increasing environmental consequences of single-use plastics. In this study, we evaluate the plastic waste management value for the state of Georgia, US and investigate the potential of introducing novel depolymerization methods within the network. An equation-based formulation is developed to identify the optimum supply-chain design given the geographic location of existing facilities. Chemical recycling technologies that have received increasing attention are evaluated as candidate technologies to be integrated within the network. The optimum supply-chain design is selected based on environmental and economic objectives. The designed network of pathways uses a mix of different technologies (chemical and mechanical recycling) in a way that are both economically environmentally sound.

Early stage exploration of reaction systems, including catalyst selection, operating conditions� specifications, reactor design, and optimization, is critical in the engineering field. It is general practice in the reaction engineering field to explore systems against certain performance metrics, of which yield is one of the most commonly utilized objectives. While the yield provides a quantitative measure of how efficiently reactants are converted into target product(s), its definition is ambiguous, particularly in the presence of side/ incomplete reactions, and multiple products. Most of the yield definitions focus on a specific target product; however, conditions within the reactor search space that provide a maximum yield for one product may not be the same as those for another. Moreover, the presence of other undesired products that are not considered may reduce the overall efficiency of the system. This necessitates the utilization of a more holistic metric that encompasses the value of all the generated products. Attempts to address this consider lumping components into a total yield metric. However, this assumes equal weights on all components without adequately capturing their individual significance on the actual performance. This study proposes the utilization of an �economic-value yield� objective that captures all the products� value by using the market price as a weight factor. The traditional yield metric for the various products is contrasted against the economic one to highlight its ability of providing insight into regions within the reactor search space that are associated with high-value products that are otherwise not observed in the conventional definition. This is illustrated with a case study utilizing propane as a feedstock in the novel piston reactor technology.

Machine learning (ML) has become a powerful tool to analyze complex relationships between multiple variables and to unravel valuable information from big datasets. However, an open research question lies in how ML can accelerate the design and optimization of processes in the early experimental development stages with limited data. In this work, we investigate the ML-aided process design of a microwave reactor for ammonia production with exceedingly little experimental data. We propose an integrated approach of synthetic minority oversampling technique (SMOTE) regression combined with neural networks to quantitatively design and optimize the microwave reactor. To address the limited data challenge, SMOTE is applied to generate synthetic data based on experimental data at different reaction conditions. Neural network has been demonstrated to effectively capture the nonlinear relationships between input features and target outputs. The softplus activation function is used for a smoother prediction compared to the Rectified Linear Unit activation function. Ammonia concentration is predicted using pressure, temperature, feed flow rate, and feed composition ratio as input variables. For point-wise prediction based on discrete operating conditions, the proposed SMOTE integrated neural network approach outperforms with 96.1% accuracy compared to neural networks (without SMOTE), support vector regression, and linear regression. The multi-variate prediction trends are also validated which are critical for design optimization.