Synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions

Recent advances in metabolic engineering have enabled the production of chemicals via bio-conversion using microbes. However, downstream separation accounts for 60–80% of the total production cost in many cases. Previous work on microbial production of extracellular chemicals has been mainly restricted to microbiology, biochemistry, metabolomics, or techno-economic analysis for specific product examples such as succinic acid, xanthan gum, lycopene, etc. In these studies, microbial production and separation technologies were selected apriori without considering any competing alternatives. However, technology selection in downstream separation and purification processes can have a major impact on the overall costs, product recovery, and purity. To this end, we apply a superstructure optimization based framework that enables the identification of critical technologies and their associated parameters in the synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions. We divide extracellular chemicals into three categories based on their physical properties, such as water solubility, physical state, relative density, volatility, etc. We analyze three major extracellular product categories (insoluble light, insoluble heavy and soluble) in detail and provide suggestions for additional product categories through extension of our analysis framework. The proposed analysis and results provide significant insights for technology selection and enable streamlined decision making when faced with any microbial product that is released extracellularly. The parameter variability analysis for the product as well as the associated technologies and comparison with novel alternatives is a key feature which forms the basis for designing better bioseparation strategies that have potential for commercial scalability and can compete with traditional chemical production methods

Closed-Loop Scheduling for Cost Minimization in HVAC Central Plants

In this paper, we examine closed-loop operation of an HVAC central plant to demonstrate that closed-loop receding-horizon scheduling provides robustness to inaccurate forecasts, and that economic performance is not seriously impaired by shortened prediction horizons or inaccurate forecasts when feedback is employed. Using a general mixed-integer linear programming formulation for the scheduling problem, we show that optimization can be performed in real time. Furthermore, we demonstrate that closed-loop operation with a moderate prediction horizon is not significantly worse than a long-horizon implementation in the nominal case, and that closed-loop operation can correct for inaccurate long-term forecasts without significant cost increase. In addition, we show that terminal constraints can be employed to ensure recursive feasibility. The end result is that forecasts of demand need not be extremely accurate over long times, indicating that closed-loop scheduling can be implemented in new or existing central plants

A Case Study of Economic Optimization of HVAC Systems based on the Stanford University Campus Airside and Waterside Systems

Commercial buildings account for $200 billion per year in energy expenditures, with heating, ventilation, and air conditioning (HVAC) systems accounting for most of these costs. In energy markets with time-varying prices and peak demand charges, a significant potential for cost savings is provided by using thermal energy storage to shift energy loads. Since most implementations of HVAC control systems do not optimize energy costs, they have become a primary focus for new strategies aimed at economic optimization. Model predictive control (MPC) has emerged as one popular method to achieve this load shifting, while respecting system constraints. MPC uses a model of the system to make predictions and to solve an optimization problem. Much research has shown the benefits of MPC over alternative strategies for HVAC control [1]. However, some industrial applications, such as large research centers or university campuses, are too large to be solved in a single MPC instance. Decompositions have been proposed in the literature, but it is difficult to evaluate and to compare decompositions against one another when using different systems. In this paper, we present a large-scale relevant case study where solving a single MPC optimization problem is neither desirable nor feasible for real-time implementations. The study is modeled after the Stanford University campus, consisting of both an airside and waterside system [2]. The airside system includes 500 zones spread throughout 25 campus buildings along with the air handler units and regulatory building automation system used for temperature regulation. The waterside system includes the central plant equipment, such as chillers, that is used to meet the load from the buildings. Active thermal energy storage is available to the campus in addition to the passive thermal energy storage present in the form of building mass. The airside models describe the temperature dynamics in each of the 500 zones, and the waterside models describe the power consumption of the central plant equipment. The aim of the control system is to minimize costs in the presence of time-varying electricity prices and a peak demand charge as well as environmental disturbances such as weather while meeting constraints on comfort and equipment. We perform an economic optimization of the entire campus using a hierarchical system with distributed airside controllers to demonstrate the potential savings. The models from this case study are made publicly available for other researchers interested in designing alternative control strategies for managing chilled water production to meet airside loads. The aim of the case study release is to provide a standardized problem for the research community. A benchmark is provided for evaluating performance. References [1] A. Afram and F. Janabi-Sharifi. Theory and applications of HVAC control systems—A review of model predictive control (MPC). Building and Environment, 72:343–355, February 2014. [2] J. B. Rawlings, N. R. Patel, M. J. Risbeck, C. T. Maravelias, M. J. Wenzel, and R. D. Turney. Economic MPC and real-time decision making with application to large-scale HVAC energy systems. Computers & Chemical Engineering, 2017. In Press

A lignocellulosic ethanol strategy via nonenzymatic sugar production: Process synthesis and analysis

The work develops a strategy for the production of ethanol from lignocellulosic biomass. In this strategy, the cellulose and hemicellulose fractions are simultaneously converted to sugars using a γ-valerolactone (GVL) solvent containing a dilute acid catalyst. To effectively recover GVL for reuse as solvent and biomass-derived lignin for heat and power generation, separation subsystems, including a novel CO2-based extraction for the separation of sugars from GVL, lignin and humins have been designed. The sugars are co-fermented by yeast to produce ethanol. Furthermore, heat integration to reduce utility requirements is performed. It is shown that this strategy leads to high ethanol yields and the total energy requirements could be satisfied by burning the lignin. The integrated strategy using corn stover feedstock leads to a minimum selling price of $5 per gallon of gasoline equivalent, which suggests that it is a promising alternative to current biofuels production approaches

LDRD final report on "fundamentals of synthetic conversion of CO2 to simple hydrocarbon fuels" (LDRD 113486).

Energy production is inextricably linked to national security and poses the danger of altering the environment in potentially catastrophic ways. There is no greater problem than sustainable energy production. Our purpose was to attack this problem by examining processes, technology, and science needed for recycling CO{sub 2} back into transportation fuels. This approach can be thought of as 'bio-inspired' as nature employs the same basic inputs, CO{sub 2}/energy/water, to produce biomass. We addressed two key deficiencies apparent in current efforts. First, a detailed process analysis comparing the potential for chemical and conventional engineering methods to provide a route for the conversion of CO{sub 2} and water to fuel has been completed. No apparent 'showstoppers' are apparent in the synthetic route. Opportunities to improve current processes have also been identified and examined. Second, we have also specifically addressed the fundamental science of the direct production of methanol from CO{sub 2} using H{sub 2} as a reductant

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Estimating Trends of Population Decline in Long-Lived Marine Species in the Mediterranean Sea Based on Fishers' Perceptions

We conducted interviews of a representative sample of 106 retired fishers in Italy, Spain and Greece, asking specific questions about the trends they perceived in dolphin and shark abundances between 1940 and 1999 (in three 20 year periods) compared to the present abundance. The large marine fauna studied were not target species of the commercial fleet segment interviewed (trawl fishery). The fishers were asked to rank the perceived abundance in each period into qualitative ordinal classes based on two indicators: frequency of sightings and frequency of catches (incidental or intentional) of each taxonomic group. The statistical analysis of the survey results showed that both incidental catches and the sighting frequency of dolphins have decreased significantly over the 60+ years of the study period (except for in Greece due to the recent population increase). This shows that fishers' perceptions are in agreement with the declining population trends detected by scientists. Shark catches were also perceived to have diminished since the early 1940s for all species. Other long-lived Mediterranean marine fauna (monk seals, whales) were at very low levels in the second half of the 20th century and no quantitative data could be obtained. Our study supports the results obtained in the Mediterranean and other seas that show the rapid disappearance (over a few decades) of marine fauna. We show that appropriately designed questionnaires help provide a picture of animal abundance in the past through the valuable perceptions of fishers. This information can be used to complement scientific sources or in some cases be taken as the only information source for establishing population trends in the abundance of sensitive species