Growth modeling to evaluate alternative cultivation strategies to enhance national microalgal biomass production

Highlights

• Cultivation strategies are evaluated for national algal biomass productivity.

• The best-performing cultivation strategies are identified for 5832 sites.

• Biomass productivity can be greatly enhanced by seasonal strain rotations.

• The optimum pond depth for biomass productivity is 15 cm for evaluated 5832 sites.

Abstract

We present a model-based assessment of alternative cultivation strategies for open pond algal biomass production within the conterminous United States (CONUS). Our assessment focuses on two basic cultivation strategies: (1) seasonal rotation of three representative freshwater algal strains that are well suited for warm weather, cold weather, and all-season diverse weather conditions, respectively; and (2) variation between three pond water depths (15 cm, 20 cm, and 25 cm). The enhanced Biomass Assessment Tool (BAT) is applied on a site-specific, hourly basis at 5832 North American Land Data Assimilation System Phase 2 1/8° meteorological model grid cells over a 30-year period (1980–2009) to evaluate the operational strategies. Recognizing that resource management decisions may also consider scales beyond the individual site, we also conduct a regional assessment focused on seven representative climate zones in the CONUS. Results demonstrate that the spatial variability of algal productivity is largely affected by the strain-specific growth response to light and temperature that vary significantly by climate zone and latitude. With a limited set of alternative cultivation strategies, the BAT identified the best-performing combination of cultivation strategies on a site-specific basis that considerably enhances national annual biomass productivity. In particular, the appropriate choice of seasonal strain rotation can significantly dampen climate-driven seasonal and spatial variability in algal productivity over the CONUS.

Introduction

Microalgae biofuels have received increased attention globally as an alternative source of energy to conventional fossil fuels in the face of rising global energy demand and concerns over greenhouse gas emissions from fossil fuel combustion that contribute to climate change, ocean acidification, and further environmental degradation [[1], [2], [3], [4], [5], [6]]. The advantages of culturing microalgae over other feedstocks for biofuel production have been described in many studies [[2], [3], [4],[6], [7], [8], [9], [10]]. Microalgae are one of the highest yielding crops with high lipid productivities in comparison to second-generation feedstocks (e.g., agricultural and forest harvesting residual biomass) that have lower conversion efficiency; microalgae are commonly cultivated in large open ponds or photo-bioreactors (PBR) with no direct competition for agricultural and biodiverse lands, and they have a lower environmental impact than first-generation feedstocks (e.g., food and oil crops such as corn and soybean) on air, water, and soil quality. Despite these advantages, some critical challenges for the development of a sustainable algal biofuel industry are related to scaling-up current domestic algae production from hundreds of acres to millions of acres of land resources, and significantly improving algae cultivation performance [[11], [12], [13], [14], [15]]. Inherent in meeting these challenges is identifying potential geographic locations for algae farms based on resource access and availability, and developing cultivation strategies that maximize climate, land, and water resource use efficiency [11,[16], [17], [18], [19], [20]].

Previous studies [6,14,16,18,[21], [22], [23], [24]] have indicated that sufficient locations have suitable climate, land, water, and nutrient resources to achieve the U.S. Department of Energy's 2017 production target of 1 million metric tons of ash-free dry weight of cultivated algal biomass per year [25]; however, climate-driven seasonal and interannual variability in biomass production presents significant economic and lifecycle challenges [26,27]. Previous studies [[28], [29], [30], [31]] estimated large-scale microalgae production potential through a range of approaches from linear scaling of laboratory-based growth to the use of simplistic growth modeling, which did not account for geographic diversity or biophysical responses to climatic differences. More recently, a global evaluation [22] of microalgae biomass productivity potential in PBR systems was conducted at 4388 global locations using a PBR biological growth model. Their study highlighted the temperature control on biomass yield and its spatial variability, and the importance of integrating geographically and temporally resolved microalgae growth modeling for estimating large-scale biomass or biofuel productivity potential. Great efforts have also been made to promote microalgae production through optimizing culture conditions such as light, temperature and nutrients, and cultivation strategies such as dilution ratio, harvest frequency, mixing and stirring degree [27,32,33]. While some strategies were able to promote biomass production to some degree, the existing literature does not provide spatially diverse and temporally consistent analysis of national-scale microalgae biomass production potential focused on operational cultivation strategies.

To fill the research gap, this study evaluates the potential of alternative cultivation strategies to enhance microalgae biomass production potential in open pond systems for the conterminous United States (CONUS).The challenges of large-scale microalgae culturing are parallel to those of operating large-scale agricultural systems throughout the world to meet the increasing demand for greater productivity and natural resource protection. Agricultural systems are operated at field to regional scales using a range of complex cultivation strategies to maximize yields such as water, temperature, and light management, co-cultivation and crop rotation. A key toolset used by the agricultural community consists of crop modeling and agricultural decision support systems to understand the diverse plant, soil, and climate interactions under various cropping and management systems over large geographic areas [[34], [35], [36]]. Representative of these systems, the Decision Support System for Agro-Technology Transfer is Geographic Information System based and consists of crop simulation models, including a database system for soil, weather, and management inputs, and weather generation programs. At the field scale, these systems can be used to determine optimal planting date(s), identify the best cultivars, analyze yields, and plan for harvest. Regional uses include assessment of alternative management practices, prediction of experimental crops in new areas, and forecasting of yields over large areas. Reinforcing the noted similarities between large-scale agricultural systems and the development of a nationally relevant algal biofuel industry, here we use the Pacific Northwest National Laboratory's (PNNL's) Biomass Assessment Tool (BAT) to investigate a limited set of cultivation strategies (i.e., seasonal strain rotation and manipulation of pond water depth) for 5832 sites across the CONUS, and evaluate their impacts on site-specific and regional microalgae production potential (Fig. 1). To enable the analysis of algal cultivation operations, two significant enhancements were made to the BAT. The first enhancement is the use of the North American Land Data Assimilation System Phase 2 (NLDAS) gridded, time-series meteorological forcing data in place of CLIGEN stochastic weather sequences derived from historical measurements [6,37]. The second enhancement allows for the use of either the original BAT growth model [6] or a more biophysically-complete growth model, herein referred to as the Huesemann Algae Biomass Growth Model [38]. Model enhancements are described in detail in Section 2. The intent of this study is to demonstrate the capability of BAT to screen cultivation strategies and to illustrate the sensitivity of biomass productivity for a limited selection of strains and operational strategies—not necessarily to determine the best available operational strategies. The presented methodology, however, has no inherent limitation on expanding the types of strategies for evaluation (e.g., dilution rate and frequency, and PBR culturing).

The remainder of this paper is organized as follows: Section 2 provides an overview of the BAT modeling system, input data, and investigated cultivation practices. Section 3 presents the model results, comparing alternative operational cultivation strategies for different climate zones across CONUS, as well as at selected site-specific locations. Key conclusions and recommended next steps for this research are presented in Section 4.