The commercial production of PHAs is commonly carried out in large scale by heterotrophic bacteria. While certain natural microalgae species are also able to generate PHAs under stress conditions, the yield is relatively low, ranging from 5 wt% (Synechocystis sp. PCC6803) (Sudesh et al., 2002) to a maximum PHA production of 69 wt% (Nostoc muscorum Agardh) (Serif et al., 2018) of dry microalgal biomass. Even with genetic engineering, microalgal production of PHA reached a peak of 85 wt% (Aulosira fertilissima CCC444) which is lower than bacterial PHA production that can reach over 95 wt% (Cupriavidus necator) of cell dry weight (CDW). However, bacterial PHA production is hindered by high price of carbon substrate. This can be solved by utilizing the more economical microalgal biomass for bacterial PHA production.

PHAs are categorized into three clusters depending on their lengths. Short chain length (scl)-PHAs contain three to five carbon atoms with the most commonly synthesized scl-PHA being the homopolymer of poly(3-hydroxybutyrate) [P(3HB)]. In contrast, medium chain length (mcl)-PHAs contain six to 15 carbon atoms. On the other hand, PHA copolymers consist of a combination of different monomer types. For instance, poly(3-hydroxyhexanoate-co-3-hydroxyoctanoate) [P(3HHx-co-3HO)] is a copolymer of mcl monomers while poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) [P(3HB-co-3HHx)] is a combination of scl and mcl monomers. In particular, scl-mcl-PHAs are highly sought for their similar biophysical properties to common plastics such as negligible water solubility with high resistance towards moisture and hydrolytic degradation (Surendran et al., 2020). The most common types of bacterial PHAs produced using microalgal feedstock are the scl copolymer poly(3-hydroxybutyrate-co-3-hyroxyvalerate) [P(3HB-co-3HV)] and poly-(R)-3-hydroxybutyrate (P3HB) (Table 1). With 60% crystallinity, P3HB, is thought to be a suitable substitute to PP. It has a melting temperature of 175°C and transition temperature of 0–9°C (Madadi et al., 2021). On the other hand, P(3HB-co-3HV) is more flexible with a melting temperature between 148 and 168°C and transition temperature of −5.5 to −2.2°C, making it more commercially profitable (Samantaray and Mallick, 2012).

The PHA synthases of heterotrophic bacteria fall under four classes; PHA synthases of class I, III, and IV polymerize scl monomers, whereas class II polymerizes mcl monomers. Due to this, heterotrophic bacteria can utilize a wide range of substrates such as monosaccharides, starch, glycerol, and fatty acids for PHA production, many of which can be derived from microalgal biomass. PHAs of different composition are produced depending on the carbon substrate supplemented as well as the PHA-producing microorganism (Surendran et al., 2020).

In general, microalgal biomass is rich in various proteins (10–47 wt% of CDW), starch components (10–20 wt% of CDW), and amylopectin (80–90 wt% of CDW), cellulose, and lipids (20–50 wt% of CDW) (Wang et al., 2016; Sun et al., 2018a; Madadi et al., 2021). Table 2 depicts the macromolecule composition of different microalgal strains. Microalgal carbohydrates are the most commonly used carbon sources from microalgal biomass for PHA-synthesizing bacteria. For instance, defatted Chlorella biomass was pretreated to yield fermentable sugars which were fed to PHA-synthesizing bacteria to produce P(3HB-co-3HV) (Naduthodi et al., 2019). Furthermore, various microalgal strains contain monosaccharides including glucose, mannose, rhamnose, galactose, arabinose, and xylose. Sucrose has also been extracted from Desmodesums and Scenedesmus (Smachetti et al., 2020). Many bacteria such as Azeobacter vinelandii and Alcaligenes latus can synthesize PHA from sucrose (Winnacker, 2019). A. latus can also utilize starch to synthesize P3HB (Chen, 2009). Galactose and glucose can be utilized by bacteria such as Hydrogenophaga pseudoflava DSM 1034 and Pseudomonas hydrogenovora (Koller et al., 2008; Povolo et al., 2013). Recently, crude glycerol from the algal biodiesel industry has been found to be a suitable feedstock for PHA production. PHB was produced by Halomonas daqingensis and Halomonas ventosae when fed with algal biodiesel waste residue that is rich in glycerol (Dubey and Mishra, 2021).

To make full use of the microalgal biomass, it is crucial to determine suitable PHA-producing bacteria that can utilize the most of the microalgal nutrients. The general population of PHA-producing microorganisms are able to utilize simple sugars and some are able to consume triglycerides while hydrocarbon utilization for PHA synthesis is rare (Jiang et al., 2016). C. necator is one of the commonly used bacterium for industrial PHA synthesis and is deemed as the model organism for PHA metabolism (Reinecke and Steinbuechel, 2009). It stores PHA up to 96 wt% of its CDW when given excess carbon source while being starved of nitrogen or phosphate. The use of genetic manipulation has further increased the commercial potential of C. necator in PHA synthesis. The glucose-utilizing mutant, C. necator NCIMB 11599, was able to accumulate PHA up to 49 wt% of CDW using the brown algae Laminaria japonica biomass as carbon source (Muhammad et al., 2020). Another strain, C. necator PTCC 1615, successfully utilized brown seaweed Sargassum sp. as feedstock for PHB production (Azizi et al., 2017). C. necator KCTC 2649 was able to produce 75.4 wt% of CDW of PHA by using 10% (w/v) of defatted Chlorella biomass (Khomlaem et al., 2021). Likewise, C. necator TISTR 1335 was fed a combination of Chlorella sp. biomass co-digested with sugarcane leaves to produce 60.9 wt% of PHA which contributes to the zero-waste generation concept (Sitthikitpanya et al., 2021).

Halophilic bacteria have gained attention as a candidate for PHA synthesis owing to their unique growth conditions that reduces the chances of contamination. Members of the halophilic Halomonadaceae family are able to amass large quantities of PHAs from different carbon sources (Abd El-Malek et al., 2021). Recently, production of biodiesel from microalgae is gaining worldwide attention as it has been shown to be the only renewable biodiesel source able to meet the global demand for transport fuels. A major by-product of this production is crude glycerol. H. daqingensis was found to be able to synthesize PHA by utilizing glycerol-rich algal biodiesel waste residue as the sole carbon source (Dubey and Mishra, 2021). Moreover, Halomonas pacifica ASL10 and Halomonas salifodiane successfully produced PHAs from the macroalgae Pterocladia capillaceais and Corallina mediterraneais as well as Arthrospira (Abd El-Malek et al., 2021).

The Paracoccus species is another potential workhorse for PHA production. In addition to the ability to switch between autotrophic and heterotrophic growth, this Gram-negative species is methylotrophic with denitrifying capabilities and as such is commonly used to treat wastewaters (Kim et al., 2015). Paracoccus denitrificans and Paracoccus pantotrophus are well studied for their ability to accumulate PHA by utilizing numerous carbon sources, such as glycerol, methanol, n-penthanol, and CO₂ (Kim et al., 2015; Chang et al., 2020). Using L. japonica biomass, Paracoccus sp. LL1 was able to synthesize PHA as well as carotenoids (Muhammad et al., 2020). Fascinatingly, this same species was able to utilize defatted Chlorella biomass to produce 37.4 wt% of CDW of PHA and 6.08 mg·L⁻¹ of carotenoids (Khomlaem et al., 2021). These findings highlight the compatibility of using microalgal biomass as feedstock for PHA production in Paracoccus.

The wild type bacterium E. coli is unable to synthesize PHA. However, transmutating the bacterium with PhaC gene allows for the production of PHA. This principle is applied for E. coli XL1-Blue harbouring phaCAB from C. necator (Spiekermann et al., 1999). By employing the aqueous fraction from an algal wet lipid extraction technique as the medium, PHB production of this particular strain saw an increase of 51% (Sathish et al., 2014). Furthermore, this same recombinant strain was also able to utilize wastewater microalgae to produce the PHB with a maximum accumulation of 31 wt% of the CDW (Rahman et al., 2015).

PHA synthesis is not only limited to Gram-negative bacteria. The Gram-positive Bacillus are also known for their PHA production, although not all genus can accumulate PHA in their cells under limiting growth environments. Bacillus pumilus (E10) isolated from the wastewaters of University of Santa Cruz do Sul was able to utilize the hydrolysate of A. platensis biomass in conjunction with glucose and glycerol to produce PHB. The soil bacterium, Bacillus megaterium ALA2, could make use of defatted Chlorella biomass and L. japonica biomass with PHA production of 29.7 wt% and 32 wt% of CDW, respectively (Muhammad et al., 2020; Khomlaem et al., 2021).

Different microalgae give rise to various types of biomasses. Choosing the right microalgae is crucial in determining the maximum biomass productivity and relative composition of the biomass constituents which then define the end product of the downstream PHA synthesis. Presently, the common wild types that provide high biomass productivity include Arthrospira, Chlorella, and Chlamydomonas reinhardtii. Figure 1 shows the morphologies of A. platensis, C. reinhardtii, and Synechocystis sp. strain PCC6803.

While optimization of microalgae growth factors is known to deliver peak biomass productivities of wild-type microalgae, bioengineering pushes these limits to overcome disadvantages that come with specific microalgae strains. Genetic engineering or genetic modification directly alters an organism’s genes via biotechnology tools. In terms of microalgae, genetic engineering aims to increase biomass productivities by modifying genes associated with photosynthesis, resistance towards extreme conditions and metabolism.

Currently, about 30 microalgae species have fully sequenced genomes. Among them are Chlorella vulgaris (Guarnieri et al., 2018), C. reinhardtii (Merchant et al., 2007), Dunaliella salina (Polle et al., 2017), Synechocystis sp. strain PCC6803 (Kaneko and Tabata, 1997), and Scenedesmus obliquus (Starkenburg et al., 2017). The pioneer of microalgae DNA modification was C. reinhardtii by Rochaix and van Dillewijn over 30 years ago (Rochaix and Van Dillewijn, 1982). Since then, more tools were developed to enhance the yield of C. reinhardtii but few of these approaches are viable for other microalgae.

Bioengineering methods widely employed for gene sequence modification in microalgae include Clustered Regularly Interspaced Short Palindromic Repeats—CRISPR associated with the protein 9 (CRISPR–Cas9) (Shin et al., 2016), zinc-finger nuclease (ZFN) (Sizova et al., 2013), Transcription Activator-Like Effector Nucleases (TALENs) (Daboussi et al., 2014) whereas RNA interference techniques such as microRNAs (RNAi) and short interfering RNAs (siRNA) are used to activate or repress expression of certain genes (Kim et al., 2015).

Compared to TALENs and ZFN, the CRISPR approach had higher applicability, allowing modulation of multiple gene expressions. However, the use of CRISPR editing for microalgae is obstructed by the toxicity of the Cas9 nuclease which results in a 10% mutation rate (Jiang et al., 2014). To overcome this, Cas9 protein-gRNA ribonucleoproteins (RNPs) provided an alternative to the lethal Cas9 (Jiang et al., 2014). Cas9 RNPs were successfully delivered into C. reinhardtii with an improved mutation rate of almost 100-fold compared to the general Cas9 approach (Shin et al., 2016). Following this success, RNPs were adopted for gene editing of other microalgae including Nannochloropsis oceanica IMET1 (Naduthodi et al., 2019), P. tricornutum (Serif et al., 2018), and Tetraselmis sp. (Chang et al., 2020).

Regardless of the recent developments in genetic engineering techniques for microalgae, bioengineering microalgae is still at its infancy stage with only a few fully sequenced genome species in addition to their complicated anatomy and physiology that hinder most genetic engineering tools. With the advancement and evolving CRISPR technology and the combination of different genetic tools, it is expected that genetic engineering will reach a breakthrough for more efficient developments of commercially-sustainable genetically engineered microalgae.

Photosynthetic organisms utilize light as the main source of energy. The most crucial aspect in microalgae growth is arguably light in the form of light limitation, saturation, and inhibition (Gatamaneni et al., 2018). The photosynthesis process necessitates both dark and light phases. In the presence of light, the light energy is absorbed by microalgae and assimilated into adenosine triphosphate (ATP) which is utilized for biomass synthesis throughout the dark cycle. As such, the dispensation of light to the cultivation system requires optimization in terms of the system’s geometric design and orientation (Fernández et al., 2001; Tredici et al., 2015). In light limiting environments, microalgae growth is proportional to the increase of light intensity. On the other hand, light saturation conditions diminish photosynthesis as the absorption of photons surpasses electron turnover (Chang et al., 2017). Further light over-exposure leads to permanent impairment to the photosynthetic system in a phenomenon termed photo-inhibition (Tredici and Zittelli, 1998). Most microalgae have a saturated photosynthesis rate at 100–500 μE∙m⁻²∙s⁻¹ and any excess light exposure will cause the microalgae to be photo-inhibited (Vejrazka et al., 2012).

Artificial or natural (solar) light sources can be utilized in cultivation systems. While solar energy is the most economically and readily available source, artificial light is preferred in high value-added cultures for the accurate regulation of photoperiod and control of the light spectrum (Schulze et al., 2014). Among the many artificial lights available such as halogen lamps and fluorescent lights, light-emitting diodes (LEDs) allows for the best modulation of light with different wavelengths (Schulze et al., 2016). The photosynthetically active radiation (PAR) that most microalgae thrive under is at the wavelength range of 380–750 nm (white light), wavelength range of 420–470 nm (blue light) and wavelength range of approximately 660 nm (red light) (Fu et al., 2013; Schulze et al., 2014). Red to far-red lights result in increased microalgae growth rate with smaller cells and reduced nutrient consumption. Blue light modifies gene expression and specific metabolic pathways resulting in an increased nutrient intake with larger cells but slower growths (Schulze et al., 2016).

Microalgae will light acclimate throughout the production phase in batch cultures; being high light (HL) acclimated consecutively after introduction with new cultures while being low light (LL) acclimated by the end of the batch cycle at high cell density (Grobbelaar et al., 1996). As a result, mega-scale microalgal cultivation systems can retain biomass at low light by maintaining biomass at high concentrations. Alternatively, small-scale microalgal cultivation systems can also employ the microalgal photo-acclimated state to attain exponentially high produce. Using a multi-compartment photobioreactor, the first layer of microalgae facing the light source were HL acclimated and consecutive layers of microalgae became gradually more LL acclimated. This continuous flow photobioreactor design takes advantage of microalgae that are acclimated simultaneously at different light conditions with productivity rates of almost 40% more than the conventional single-layered perpendicular plate reactor (Grobbelaar and Kurano, 2003).

Recent years have also seen an increase in studies on other novel strategies to boost light utilization by microalgae and thereby increasing productivities. Table 3 shows a number of these approaches. In raceway ponds, addition of light filters increased the productivity of Chlorella (32.6% increase in biomass productivity) and D. salina (68% cell weight increment) (Cheng et al., 2015b; Nwoba et al., 2021). When grown in photobioreactors with light/dark regulation, Chlorella experienced a 21.6% increase in biomass productivity. The use of light-splitting/light-harvesting additives also aids in increase in biomass productivities. Silicon dioxide nanoparticles increased biomass productivity of Scenedesmus by 22.3% while calcium carbonate crystals raised biomass activity of Neochloris oleoabundans by 31.5% (Hong et al., 2019; Ren et al., 2020).

Rise in light intensity yielded an increase in carbohydrate generation with light intensity in the range of 30–400 μmol·m⁻²·s⁻¹ boosts the accumulation of carbohydrates. For instance, S. obliquus CNW-N proved that there was a positive association between biomass/carbohydrate productivities and light intensity prior to photoinhibition; high light intensity of 420 μmol·m⁻²·s⁻¹ led to peak carbohydrate productivity of 0.322 g·L⁻¹∙d⁻¹ which was higher than that of Tetraselmis subcordiformis (0.256 g·L⁻¹∙d⁻¹) (Zheng et al., 2011; Ho et al., 2012).

Temperature regulates biochemical processes of microalgae, particularly the gross photosynthetic rate through cellular division, which in turn affects biomass production. Culturing microalgae at lower temperatures than optimum will affect photosynthesis as carbon assimilation activity is reduced while overheating degrades photosynthetic proteins which lowers photosynthetic rates and thereby shrinks microalgae cells (Anjos et al., 2013). Optimal temperatures for most microalgae species range between 20 and 35°C (Xu et al., 2010; Cho et al., 2011) but certain thermophilic species such as Anacystis nidulans can tolerate up to 40°C (Caïa et al., 2018).

Microalgae typically absorb radiated heat from the light source. Additionally, microalgae growth is highly exothermic with over 95% of light absorbed converted into heat (Cheng et al., 2015a). Small-scale microalgae cultivation systems may not require temperature control as heat is released to the environment through convection when the surrounding setting is cold enough. However, mega-scale outdoor cultivation of microalgae is constantly exposed to solar radiation which directly heats cultures (Chiang et al., 2011). In essence, closed systems are inclined to overheat while open systems experience high water evaporation rates under intense irradiance (Gonzalez-Camejo et al., 2020). Since higher temperatures are more lethal to microalgae than lower temperatures, culture cooling is employed particularly with closed photobioreactors. Nevertheless, lower temperatures (<10°C) result in reduced biomass productivities (Thompson, 1996). Furthermore, temperature highly affects enzymes involved in starch production such as starch synthase and sucrose synthase. When temperature was increased from 5 to 20°C, carbohydrate content in C. vulgaris SO-26 plummeted from 70 to 50% (Madadi et al., 2021). As such, depending on the surrounding climate, heating during winter and cooling during summers are advantageous to microalgae culturing.

Much effort was made to identify the best method to prevent overheating of cultures in large-scale settings. The typical approach is using water sprays to sprinkle the surface of the photobioreactors with heated or cooled liquid but this is only suitable for sites of low air humidity (Cheng et al., 2019b). Heat exchangers are also widely employed to dissipate excess heat to large water bodies. Both open and closed cultivation systems in temperate regions can be housed in greenhouses (Gerardi, 2015; Cheng et al., 2019a).

Recent technologies have allowed for in-depth studies on the effects of temperature on microalgae culture conditions; be it in a controlled laboratory setting, outdoor systems, photobioreactor simulations, and theoretical models. For instance, cultivation of Arthrospira platensis in winter saw an increase of phycocyanin productivity when the microalgal was grown in a thermally-insulated photobioreactor complimented by photovoltaic plate incorporation (Gensemer et al., 1993). Another study reported increased photosynthetic conversion efficiency of over 7% from cultures with Scenedesmus and Chlorella species when grown in a photobioreactor fixed with a double-wall hose structure combined with temperature control in a closed system (Anderson and Morel, 1978). Furthermore, thermal modeling aids in simulating and optimizing various reactor designs. One such model delved into the effect of different flat-plate reactor designs which identified accurate predictions of monthly energy consumption needed to regulate the temperature of microalgae cultures (Rotatore and Colman, 1991).

Microalgal biomass production is unique as microalgae are capable of fixing carbon dioxide (CO₂). As mentioned, the CO₂ fixing efficiency of microalgae is higher than terrestrial plants. Consequently, carbon dioxide is one of the limiting reactants in photosynthesis. Concentrations of CO₂ needed for peak photosynthetic efficiency is 1–5 vol% (Solimeno et al., 2015). Given this, the available CO₂ in the air of only 0.04 vol% is insufficient for high productivity (Xu et al., 2010). CO₂ can be supplied as atmospheric air, commercially purified CO₂, raw flue gas or via supplementation of salts, for instance, bicarbonates (Cho et al., 2011).

The demand for CO₂ for microalgae growth is at its highest during the day when photosynthetic activity is active, while there is zero demand at night. CO₂ delivery approaches must manage these demands that fluctuate seasonally and diurnally. Open raceway ponds and closed systems often use spargers or diffusers to deliver CO₂ (Chiang et al., 2011; Anjos et al., 2013). The sparger injects CO₂ in gas bubbles at the bottom of the pond while a paddlewheel circulates CO₂ throughout the microalgae culture (Cheng et al., 2015a). By studying CO₂ transfer rates in an open algal pond, it was found that decelerating the paddle wheel rotation speed to 13 rpm decreases CO₂ and losses up to 61% (Caïa et al., 2018). Additionally, to achieve the maximum CO₂ utilization efficiency, CO₂ must be extracted from gas bubbles before the bubbles escape to the pond surface. Sumps were introduced at the point of gas injection to lengthen the retention time of bubbles (Thompson, 1996). Another strategy was to minimize the bubble size. Microbubbles of diameters not larger than 100 μm have a higher surface-to-volume ratio and rise slowly to the surface of culture, allowing more CO₂ to disperse throughout the medium (Gonzalez-Camejo et al., 2020). Novel photobioreactors designs have considered these factors when integrating technologies to increase CO₂ utilization efficiency. Some examples include the jet-aerated tangential swirling-flow plate photobioreactor (Cheng et al., 2019a) that condenses bubble diameter and CO₂ microbubbles dissolver (CMD) (Cheng et al., 2019b) that facilitates dissolved CO₂ in photobioreactors.

Hydrogen potential (pH) is known to affect not just the microalgae but also influences the mineral and carbon dioxide solubility of the media. Most microalgae tolerate a pH range of 6–10 (Yang et al., 2011). At extremely acidic conditions, the absorption rate of nutrients and trace metals is altered which might cause metal toxicity (Anderson and Morel, 1978; Gensemer et al., 1993). On the other hand, extremely alkaline conditions cause enzyme degradation as well as lowering microalgae affinity towards free CO₂ (Rotatore and Colman, 1991). During microalgal photosynthesis at optimal pH, available bicarbonate in the medium is transformed into CO₂ which releases hydroxyl ions. These excess hydroxyl ions increase the pH of the medium (Gerardi, 2015). Carbon at alkaline conditions is present in the form of carbonates and is not favored by microalgae (Solimeno et al., 2015). Supplementation of CO₂ acidifies the microalgal medium by altering carbonate balance (Galloway and Krauss, 1961). Consequently, controlled injection of CO₂ is required to maintain optimal pH levels of the medium. The employment of sensors to monitor pH levels has also benefitted microalgae cultivation as seen in a model that observed C. reinhardtii cells (Ifrim et al., 2014). Such pH monitors can also aid in determining photosynthetic productivity (Gonzalez-Camejo et al., 2020).

Understanding the key nutrients for microalgae growth would aid in maximizing biomass productivity as well as the production of favored synthesis. Microalgae demonstrate significant fluctuations in biochemical compositions when cultured under different limiting nutrients. Nutrient requirements of microalgae are calculated based on the formula of CO_0.48H_1.83N_0.11P_0.01 (Ifrim et al., 2014). The three non-mineral nutrients essential for photosynthesis are carbon, oxygen, and hydrogen. Carbon is required in bulk as it is a major component of all organic substances including proteins, carbohydrates, lipids and even nucleic acids (Ifrim et al., 2014). Autotrophic microalgae entail inorganic carbon sources in the forms of CO₂, carbonate, and bicarbonate while heterotrophic microalgae can use acetate or glucose (Ifrim et al., 2014).

Nitrogen is the second most element in the microalgal biomass which makes up for 7–20% of the dry cell weight (Ifrim et al., 2014). It is the building block of all structural and functional proteins. Nitrogen-depleted microalgae are inclined to carbohydrate synthesis (Ifrim et al., 2014). By limiting NaNO₃, NaH₂PO₄, metals, and vitamins on Tetraselmis sp., starch content peaked at 42% of CDW (Dammak et al., 2017). Similarly, starch content of C. vulgaris and Chlorella zofingiensis increased over 40% and 66%, respectively when starved of nitrogen (Dragone et al., 2011). Nitrogen limitation was also shown to be the most superior inducer of carbohydrate accumulation compared to limiting sulphur or phosphorus (Brányiková et al., 2011; Yuan et al., 2018). Parachlorella kessleri that were starved off of nitrogen, phosphorus or sulphur experienced a spike in carbohydrate content for the first few days before dropping. This was then followed by lipid accumulation. The initial reaction might be due to carbohydrate synthesis as energy reserves in response to the initial stress before the same energy is utilized for lipid synthesis if starvation was prolonged (Shaikh et al., 2019).

Phosphorus is a core macronutrient involved in ATP biosynthesis, nucleic acid formation, growth and cellular maintenance. Starvation of inorganic phosphate in Chlorella sp. FC2IITG exhibited high carbohydrate content up to 47.35 wt% of CDW (Muthuraj et al., 2014). Deprivation of phosphate on Leptolyngbya limnetica and Oscillatoria obscura resulted in increased carbohydrate levels of 44.5 wt% and 40.4 wt% of CDW, respectively which were 45% more than carbohydrate levels under nitrogen limitation (Kushwaha et al., 2018).

Additionally, trace elements such as sulphur, copper, and manganese play significant roles in microalgae growth. Sulphur starvation stops cellular metabolism while amassing compounds such as carbohydrates. C. vulgaris was reported to amass 60% of carbohydrates when sulphur was limited (Brányiková et al., 2011). Starving of manganese and potassium also instigated an increase in carbohydrate content in C. reinhardtii (Markou et al., 2012). Calcium and magnesium limitation also increased carbohydrate concentration of Chlorella sorokiniana by 50% without compromising biomass productivity (Hanifzadeh et al., 2018). Conversely, exposure to high copper concentration induced carbohydrate production in diatoms Cylindrotheca fusiformis and Gymnodimium sp. (Pistocchi et al., 2000). Likewise, high carbohydrate accumulation was reported when iron was in excess in tandem with nitrogen limitation and high light illumination (Yeesang and Cheirsilp, 2011).

In addition to moderating microalgal growth parameters, an alternative technique in refining biomass production is the provision of chemicals. Chemical modulators do not compromise microalgal growth that is observed in nutrient deficiency nor does it necessitate specific data on molecular targets that is required in the genetic engineering method. Instead, chemical modulators are naturally occurring molecules in microalgae that rely on phenotypic screening to target the cellular functions, growth, and metabolism of the microalgae. For instance, a large scale phenotypic screening of 42 chemicals on their functions in microalgal lipid metabolism identified 12 modulators that enhanced over 100% of intracellular lipid levels in addition to successfully up-scaling the use of two chemicals, propyl gallate and butylated hydroxyanisole (Franz et al., 2013). Similarly, 10 chemical modulators were screened on Scenedesmus dimorphus UTEX1237 and 6 were found to enhance carbohydrate productivity: butylated hydroxyanisole, forskolin, acetylcholine, brefeldin, propyl gallate, and jasmonic acid (Paliwal and Jutur, 2021). In the same study, butylated hydroxyanisole, forskolin, acetylcholine, brefeldin and propyl gallate are economically feasible. Additionally, naphthoxyacetic acid or jasmonic acid enhanced lipid accumulation of Schizochytrium sp. S31 by 11.16% and 12.71%, respectively (Wang et al., 2018). As chemical modulators either act directly on a target enzyme or functions as signal molecules, a novel approach termed “chemical modulator based adaptive laboratory evolution” was developed for Crypthecodinium cohnii (Diao et al., 2019). By using the chemical sesamol, this study found that quenching of reactive oxygen species enhanced central carbohydrate and energy metabolism. Conversely, the application of γ-Aminobutyric acid (GABA) along with nitrogen starvation was found to improve starch content of Tetraselmis subcordiformis by 23.4% and starch yield by 28.6% presumably by reducing reactive oxygen species (Ran et al., 2020). Considering their low-cost, chemical modulators are appropriate treatments for sustainable microalgal biorefinery.

There are generally two classes of microalgae cultivation systems available: open raceway pond (RWP) and closed photobioreactor (PBR). Closed systems allow for precise control of growth parameters as well as minimal risk of biological and non-biological contamination. Alternatively, open systems make use of simple designs, natural solar illumination, and lower operating costs. Table 4 depicts a summarized comparison between the available microalgae cultivation systems.

Harvesting is the process of separating microalgae from their growth media. This generally involves the elimination of water from the microalgal medium which thereby concentrates the biomass. Due to the minute sizes of microalgae (diameters of 3–30 m), their cell density is similar to water which poses a challenge in the recovery process (Moreno-Garrido, 2008). Harvesting cost accounts for at least 20% of total microalgal biomass cost and can even reach up to 90% of total costs for open RWPs (Rodolfi et al., 2009; Hulatt et al., 2017). As such, the selection of a suitable harvesting method must consider the overall energy consumption and properties of the chosen microalgal, cell size and density, final product specifications and reusability of the culture medium (Quinn et al., 2012; Ma et al., 2014).

Microalgal biomass obtained directly from dewatering is usually dried before subsequent downstream processes as the dry solid content is low (Starkenburg et al., 2017). Drying inhibits microbial spoilage, increases the shelf life of the biomass, and lessens the costs of packing, handling, transportation and storage of the microalgal biomass (Bennamoun et al., 2013). An ideal drying method should dehydrate cells while reducing as much deterioration as possible to the delicate microalgal cells. Moreover, the time taken and costs incurred for the drying process must also be taken into consideration (Hosseinizand et al., 2018).

Traditional solar drying is the most common and cost-saving method as it utilizes direct energy from the sun (Marles et al., 2011). Drying from solar energy does not alter the composition of the microalgal biomass, as such is ideal for downstream processing of PHAs. However, it requires long drying periods with large drying surface areas, in addition to difficult quality maintenance due to biomass degradation and a high risk of bacterial contamination in open conditions as well as overheating (Shin et al., 2016). Open solar drying was also found to decrease as much as 40% in polyunsaturated fatty acids from Derbesia tenuissima biomass (Sizova et al., 2013). Closed solar drying systems that employ solar water heating systems have been developed that increase environmental temperatures to 60°C. Such drying methods can dry biomass to 10% water content within 5 h (Rochaix and Van Dillewijn, 1982). On the other hand, freeze-drying preserves cell constituents. The freeze-drying process was able to retain over 90% of protein composition (Shin et al., 2016). Regardless, this method aggregates more cells in crystal form resulting in a smaller cell surface area of contact with the extracting solvent, which would have significant effects on the cell wall integrity (Sizova et al., 2013). Spray drying is also utilized for many microalgae species (Hosseinizand et al., 2018). Here, atomized water droplets are sprayed into a vertical tower while hot gas is passed down and the dried biomass is collected from the tower base (Soeder, 1980). While drying is achieved within seconds, high pressure from the atomization procedure could damage cells and cause degradation (Show et al., 2019). Due to this atomization process, spray drying requires high energy demand and high capital (Chen et al., 2015).

Regardless of the many drying techniques available, the mechanism of how remaining water in the microalgal biomass affects nutrient extraction such as carbohydrates is not fully understood. There is much debate on whether this step is necessary. Water creates a barrier that prevents the effective nutrient mass transfer from the cells to the extraction solvent and is thus thought to be an indispensable process (Amaro et al., 2017). On the other hand, another hypothesis claims that the presence of water in the biomass improves nutrient extraction efficiency and can be removed (Medina et al., 1998). Indeed, nutrient extraction such as carbohydrates and lipids from wet microalgal biomass devoid of drying has been fruitful (Vieira et al., 2021). Since the drying step requires at least 89% of energy demand and incurs up to 75% of total processing cost, eliminating this step could prove economical and time-saving for large-scale production of microalgal biomass (Taher et al., 2014).