Global optimization for sustainable design and synthesis of algae processing network for CO2mitigation and biofuel production using life cycle optimization

Transformation and Linearization Techniques in Optimization: A State-of-the-Art Survey

To formulate a real-world optimization problem, it is sometimes necessary to adopt a set of non-linear terms in the mathematical formulation to capture specific operational characteristics of that decision problem. However, the use of non-linear terms generally increases computational complexity of the optimization model and the computational time required to solve it. This motivates the scientific community to develop efficient transformation and linearization approaches for the optimization models that have non-linear terms. Such transformations and linearizations are expected to decrease the computational complexity of the original non-linear optimization models and, ultimately, facilitate decision making. This study provides a detailed state-of-the-art review focusing on the existing transformation and linearization techniques that have been used for solving optimization models with non-linear terms within the objective functions and/or constraint sets. The existing transformation approaches are analyzed for a wide range of scenarios (multiplication of binary variables, multiplication of binary and continuous variables, multiplication of continuous variables, maximum/minimum operators, absolute value function, floor and ceiling functions, square root function, and multiple breakpoint function). Furthermore, a detailed review of piecewise approximating functions and log-linearization via Taylor series approximation is presented. Along with a review of the existing methods, this study proposes a new technique for linearizing the square root terms by means of transformation. The outcomes of this research are anticipated to reveal some important insights to researchers and practitioners, who are closely working with non-linear optimization models, and assist with effective decision making.

Energy and environmental sustainability of waste personal protective equipment (PPE) treatment under COVID-19

Combating the COVID-19 pandemic has raised the demand for and disposal of personal protective equipment in the United States. This work proposes a novel waste personal protective equipment processing system that enables energy recovery through producing renewable fuels and other basic chemicals. Exergy analysis and environmental assessment through a detailed life cycle assessment approach are performed to evaluate the energy and environmental sustainability of the processing system. Given the environmental advantages in reducing 35.42% of total greenhouse gas emissions from the conventional incineration and 43.50% of total fossil fuel use from landfilling processes, the optimal number, sizes, and locations of establishing facilities within the proposed personal protective equipment processing system in New York State are then determined by an optimization-based site selection methodology, proposing to build two pre-processing facilities in New York County and Suffolk County and one integrated fast pyrolysis plant in Rockland County. Their optimal annual treatment capacities are 1,708 t/y, 8,000 t/y, and 9,028 t/y. The proposed optimal personal protective equipment processing system reduces 31.5% of total fossil fuel use and 35.04% of total greenhouse gas emissions compared to the personal protective equipment incineration process. It also avoids 41.52% and 47.64% of total natural land occupation from the personal protective equipment landfilling and incineration processes.

Accelerating Manufacturing for Biomass Conversion via Integrated Process and Bench Digitalization: A Perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass...

Biogas Reforming as a Precursor for Integrated Algae Biorefineries: Simulation and Techno-Economic Analysis

Biogas is a significant by-product produced in algae processing and may be used for many different applications, not only as a renewable energy carrier but also as a chemical intermediate in integrated algae-based biorefineries. In this work, the reforming of biogas to H2/CO2 mixtures (referred to as SynFeed) as feed for the direct hydrogenation of CO2 to methanol is investigated. Two conventional processes, namely steam methane and autothermal reforming, with upstream CO2 separation from raw biogas are compared to novel concepts of direct biogas bi- and tri-reforming. In addition, downstream CO2 separation from SynFeed using the commercial Selexol process to produce pure H2 and CO2 is considered. The results show that upstream CO2 separation with subsequent steam methane reforming is the most economic process, costing 142.48 €/tSynFeed, and taking into consideration the revenue from excess hydrogen. Bi-reforming is the most expensive process, with a cost of 413.44 €/tSynFeed, due to the high demand of raw biogas input. Overall, SynFeed from biogas is more economical than SynFeed from CO2 capture and water electrolysis (464 €/tSynFeed), but is slightly more expensive than using natural gas as an input (107 €/SynFeed). Carbon capture using Selexol comes with costs of 22.58–27.19 €/tCO2, where approximately 50% of the costs are derived from the final CO2 compression.

Waste respirator processing system for public health protection and climate change mitigation under COVID-19 pandemic: Novel process design and energy, environmental, and techno-economic perspectives

The ongoing COVID-19 pandemic leads to a surge on consumption of respirators. This study proposes a novel and effective waste respirator processing system for protecting public health and mitigating climate change. Respirator sterilization and pre-processing technologies are included in the system to resist viral infection and facilitate unit processes for respirator pyrolysis, product separation, and downstream processing for greenhouse gas (GHG) emission reduction. We evaluate the system's environmental performance through high-fidelity process simulations and detailed life cycle assessment. Techno-economic analysis results show that the payback time of the waste respirator processing system is seven years with an internal rate of return of 21.5%. The tipping fee and discount rate are the most influential economic factors. Moreover, the unit life cycle GHG emissions from the waste respirator processing system are 12.93 kg CO₂-eq per thousand waste respirators treated, which reduces GHG emissions by 59.08% compared to incineration-based system so as to mitigate climate change.

Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products

With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a "domino effect" in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.

Life Cycle Assessment for the Design of Chemical Processes, Products, and Supply Chains

Design in the chemical industry increasingly aims not only at economic but also at environmental targets. Environmental targets are usually best quantified using the standardized, holistic method of life cycle assessment (LCA). The resulting life cycle perspective poses a major challenge to chemical engineering design because the design scope is expanded to include process, product, and supply chain. Here, we first provide a brief tutorial highlighting key elements of LCA. Methods to fill data gaps in LCA are discussed, as capturing the full life cycle is data intensive. On this basis, we review recent methods for integrating LCA into the design of chemical processes, products, and supply chains. Whereas adding LCA as a posteriori tool for decision support can be regarded as established, the integration of LCA into the design process is an active field of research. We present recent advances and derive future challenges for LCA-based design.

Design and optimization of poly(hydroxyalkanoate)s production plants using alternative substrates

In this work, we propose a Mixed Integer Nonlinear Programming (MINLP) model to determine the optimal design of a poly(hydroxyalkanoate)s (PHAs) production plant configuration. 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 enzyme, 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. The resulting MINLP model maximizes the project net present value (NPV) as objective function and it is implemented in an equation oriented environment. Optimization results show the sugarcane-enzyme option as the most promising alternative (NPV = 75.01 million USD) for PHAs production with an energy consumption of 22.56 MJ/kg PHA and a production cost of 3.02 USD/kg PHA. Furthermore, an economic sensitivity analysis is performed.

Evaluation of Marine Synechococcus for an Algal Biorefinery in Arid Regions

Implementing microalgae biorefinery in arid environments requires utilization of strains that can grow at high temperatures (above 28 °C) and salinity levels (above 30 ppt). In this study, we investigate the newly isolated seawater strain, Synechococcus, native to the United Arab Emirates, and evaluate its value as a perspective organism for cultivation (for fuel and bio-products) in regions with freshwater scarcity. The strain displayed tolerance to a wide range of temperature (22–37 °C) and salinity (20–41 ppt), with maximum biomass concentration of 0.72 g L−1 and a maximum growth rate of 82 mg L−1 d−1 at 25 °C and 33 ppt salinity. Lipids accumulation reached up to 26% of dry weight in nitrogen-depleted conditions (with 1.8 mM of nitrates addition to the media), whereas protein content exceeded 50% dry weight. In this study, harvesting is investigated using three chemical agents: Ferric chloride, sodium hydroxide, and chitosan. Cell disruption is analyzed for four distinct treatments: Enzymatic, alkaline, ultrasonic, and hydrothermal. Among tested methods, flocculation with sodium hydroxide and ultrasonication were found to be the most efficient techniques for harvesting and cell disruption, respectively. The growth characteristics of the local strain and the potential to derive protein and lipids from it makes it a promising biomass in a biorefinery context.

Integrating Genome-Scale and Superstructure Optimization Models in Techno-Economic Studies of Biorefineries

Genome-scale models have become indispensable tools for the study of cellular growth. These models have been progressively improving over the past two decades, enabling accurate predictions of metabolic fluxes and key phenotypes under a variety of growth conditions. In this work, an efficient computational method is proposed to incorporate genome-scale models into superstructure optimization settings, introducing them as viable growth models to simulate the cultivation section of biorefinaries. We perform techno-economic and life-cycle analyses of an algal biorefinery with five processing sections to determine optimal processing pathways and technologies. Formulation of this problem results in a mixed-integer nonlinear program, in which the net present value is maximized with respect to mass flowrates and design parameters. We use a genome-scale metabolic model of Chlamydomonas reinhardtii to predict growth rates in the cultivation section. We study algae cultivation in open ponds, in which exchange fluxes of biomass and carbon dioxide are directly determined by the metabolic model. This formulation enables the coupling of flowrates and design parameters, leading to more accurate cultivation productivity estimates with respect to substrate concentration and light intensity.

Integrating Hybrid Life Cycle Assessment with Multiobjective Optimization: A Modeling Framework

By combining life cycle assessment (LCA) with multiobjective optimization (MOO), the life cycle optimization (LCO) framework holds the promise not only to evaluate the environmental impacts for a given product but also to compare different alternatives and identify both ecologically and economically better decisions. Despite the recent methodological developments in LCA, most LCO applications are developed upon process-based LCA, which results in system boundary truncation and underestimation of the true impact. In this study, we propose a comprehensive LCO framework that seamlessly integrates MOO with integrated hybrid LCA. It quantifies both direct and indirect environmental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria. The proposed LCO framework is demonstrated through an application on sustainable design of a potential bioethanol supply chain in the UK. Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO. Among the biomass feedstock options considered, using woody biomass for bioethanol production would be the most preferable choice from a climate perspective, while the mixed use of wheat and wheat straw as feedstocks would be the most cost-effective one.