Jorquera and colleagues compared the energy life-cycle analysis for the production of microalga Nannochloropsis sp. in flat-plate photobioreactors (FPPR), tubular photo­bioreactors (TPR), and raceway ponds (RP) [485] . The NER of these systems was estimated and compared as total energy produced (energy of biomass) over the energy content of the materials, energy required for construction, and the energy required for operation. A preliminary analysis (including energy costs for pumping, mixing, and gas transfer) showed that the NER value of biomass production in the TPR is 0.2. Consequently, the energy demand is larger than the energy content of the produced biomass in the TPR. The estimated NERs for biomass production in the FPPR and RP are 4.51 and 8.34, respectively [485] . The authors also performed a detailed energy life-cycle analysis using the GaBi program and achieved NER values 4.33 and 7.01 for the FPPR and RP, respectively. This result is in agreement with data achieved for the production of Dunaliella biomass where raceway ponds were found to be more efficient [486]. Indeed, the production of C. vulgaris in raceway bioreactors is more environ­mentally sustainable compared to the production in air-lift tubular reactors [487].

The concerted international effort during the last decade has led to significant prog­ress and important advances [125, 127]. While most of the most important issues affecting gas production from geological GH systems have been addressed or are being addressed, some important issues need further attention and examination. Below we discuss the remaining challenges.

5.2 Challenges in the Analysis and Interpretation of Geophysical Surveys

This issue has been the subject of recent extensive review articles. The interested reader is directed to the papers of Bellefleur et al. [5] , Dvorkin and Uden [42] , Hardage et al. [62], Lee et al. [103], Inks et al. [71], and Boswell [9] for detailed discussions. In general, the current consensus is that GH exploration is fairly well advanced when assessing deep deposits at high SH, but challenges remain with respect to shallow hydrates and GH of complex geometries. The well-log analysis following the 2002 Mallik Test [35] still represents the state of the art in this subject. In log analysis, the approach appears to be well established for the sand reservoirs, but for grain-displacing, clay-hosted hydrates, further work is needed [102].

The current bottleneck in the state-of-practice of geophysical analysis is centered on the relationship between measured physical parameters and SH. In particular [103, 173]:

• Electrical conductivity and Archie’s Law: most applications use empirical and fitting parameters, leading to good fits to the data but uncertain predictions.

• P-wave velocity data—Biot-Gassman. The constrained modulus is strongly affected by the stiffness of the pore fluid, leading to potential error magnification in the assessment of SH from Vp.

• S-wave velocity dependence on SH and pore habit: proper constitutive models are needed for effective stress-dependent sediment shear stiffness and to account for the impact of hydrate on skeletal stiffness.

Laboratory-scale, borehole-scale and field-scale geophysical measurements are all conducted at different frequencies and wavelengths, hence they sense different temporal and spatial conditions, and caution is advised in the interpretation of mea­surements. An even bigger challenge is the need to expand to integrated geophysical analyses that include multicomponent seismic surveys [63, 174] . in combination with promising EM techniques [43, 163, 201, 211, 175].

Current US electricity generation (4.2 x 1012 kWh, [46]) could produce roughly 24% of the Nation’s crude oil demands; the US installed base operating at 100% utiliza­tion (1.12 TW nameplate capacity, [15]) could produce roughly 55% of the Nation’s crude oil demands (Supp Calc 5). The economical production of fuel at even moder­ate scales would require significant renewable energy feedstocks: at least 1 GW of renewable electricity would be required to synthesize 10,000 BOE/day via an Electrofuels approach (Supp Calc 5). No electrochemical process has ever approached such scales and implementation will clearly require significant advances in engineering. Still, the potential advantages of an Electrofuels platform for the production of liquid fuels through processes that avoid the constraints of photosyn­thetic approaches is hard to overstate; replacement of 100% of the Nation’s gasoline demand using 20% efficient solar collectors could be achieved harvesting the solar resource of approximately 7,000 mi2 of nonarable land (Supp Calc 10), an area less than 5% of the acreage currently used for corn production in the USA [54].

4 Conclusions

The Electrofuels program offers an opportunity to transform the US energy infra­structure. By converting renewable electricity at times of low demand into fungible liquid fuels, engineered chemolithoautotrophic organisms could enable further inte­gration of wind and solar energy on the electricity grid while diminishing US depen­dence on foreign oil. The technical challenges remain significant, and the approach is still in its infancy. Those challenges notwithstanding the use of nonphotosynthetic autotrophic organisms offers a feedstock flexible and potentially high efficiency synthesis of liquid fuels directly from renewable energy resources, without compe­tition for arable land or scarce water resources. Continued development of the Electrofuels program offers a hedge against future resource constraints and the costly and unpredictable supply of foreign oil.

The Advanced Research Projects Agency (ARPA-E). ARPA-E was created by the US Congress in 2007 to enhance our energy and economic security, strengthening national security through the way we generate, store, and use energy. ARPA-E invests in and manages the development of transformational energy technologies that hold the potential to radically shift our Nation’s energy reality. Modeled after the Defense Advanced Research Projects Agency (DARPA), ARPA-E considers high-risk/high-impact routes to energy innovation. ARPA-E aims to promote the rapid development of technologies toward a point where private investment funds demonstration at scale and deployment.

Acknowledgments We thank Dr. Nicholas Cizek, Dr. Philippe Larochelle, Dr. Dawson Cagle, and Mr. Gregory Callman for their contributions to this chapter. We thank the 13 ARPA-E Electrofuels performer teams whose efforts make these goals possible.

Appendix A. Supplementary Calculations

A.1 Supplementary Calculation 1: Energy Captured by Corn-to-Ethanol

Critical values

Annual solar radiance: 4.1 kWh/m2/day (Des Moines, IA—[40]) Corn yield Midwest: 152.8 bushels/ac-yr [55]

Corn-to-ethanol yield: 2.8 gal ethanol/bushel corn [6, 10]

Annual solar energy

(4.1kWh/m2 /day) x (3.6 x 106 J/kWh) x (4,047m2 /ac) x (365days/yr) = 2.2 x 1013 J/ac — yr

Annual processed fuel energy

(152.8bushels corn/ac — yr) x (2.8gal EtOH/bushel corn) x(3.785L EtOH/gal EtOH) x (24x 106 J/L EtOH)

= 3.9x 1010 J/ac — yr

Fraction solar energy harness in ethanol fuel (3.9 x 1010 J/ac — yr) / (2.2 x 1013 J/ac — yr) = 0.18%

A.2 Supplementary Calculation 2: Energy Captured by Sugarcane-to-Ethanol

Critical values

Annual solar radiance: 5.6 kWh/m2/day (Brazil—[21, 23])

Brazil sugarcane yield: 33.4 tons harvested/ac-yr [55] Sugarcane-to-ethanol yield: 19.5 gal ethanol/ton sugarcane [53]

Annual solar energy

(5.6kWh/m2 /day) x (3.6 x 106 J/kWh) x (4,047m2 /ac) x (365days/yr) = 3.0 x 1013 J/ac — yr

Annual processed fuel energy

(33.4tons sugarcane/ac — yr)x (19.5gal EtOH/ton sugarcane) x(3.785L EtOH/gal EtOH) x (24x 106 J/L EtOH)

= 5.9 x 1010 J/ac — yr

Fraction solar energy harnessed in ethanol fuel (5.9 x 1010 J/ac — yr) / (3.0 x 1013 J/ac — yr) = 0.20%

A.3 Supplementary Calculation 3: Minimum Water Used by Electrofuels

Critical values

4CO2 + 5H2O + 28e — ^ 1C4H10O + 6O2 (Electrofuels cell metabolism, 20e — to convert 2CO2 to 2AcCoA, 8e — to convert 2AcCoA to 1BuOH, assuming no diverted energy)

Minimum water use

(5mol H2O/1mol BuOH)x (18g H2O/1mol H2O) x (1mol BuOH/74g BuOH) x(1,000g BuOH/36.6 x 106 J) x (1L H2O/1,000g H2O) x (34.2 x 106 J/L gasoline)

= 1.1gal H2O/gal of gasoline equivalent

A.4 Supplementary Calculation 4: Solar Efficiency of Electrofuels

Critical values

4CO2 + 5H2O + 28e — ^ 1C4H10O + 6O2 (Electrofuels cell metabolism) Efficiency of solar panel: 20% [25]

Voltage of delivered electricity: 1.5 V [39]

Solar efficiency of electrofuels

(0.2J e- /J solar energy) x (1C(@1.5V) /1.5J e-) x (6.24 x 1018 e — /C) x(1BuOH/28 e-) x (1mol BuOH/6.02 x 1023BuOH) x(74g BuOH/mol BuOH) x (36.6 x 106 J/1,000g BuOH)

= 13.3%

= 29.1%

(with state of art 43.5% solar cell, [25])

A.5 Supplementary Calculation 5: Land Requirements for Fuels Production

Critical values

Electrofuels solar efficiency: 13.3% (Supp Calc 4)

Joule unlimited solar efficiency: 7.2% [49]

Annual solar radiance: 5.7 kWh/m2/day (El Paso, TX — [40])

Land use requirements

(5.7kWh/m2 /day) x (3.6 x 106 J/kWh) x (4,047m2 /ac) x(1BOE/6.12 x 109J) x (365days/yr)

= 4,950BOE/ac — yr(solar energy)

= 660BOE/ac — yr(Electrofuels@13.3% Efficiency-Wood-Ljundahl)

= 360BOE/ac — yr(Electrofuels@7.2% Efficiency-3 — hydroxypropionate bicycle or Calvin cycle, anaerobic)

= 360BOE/ac — yr(Joule Unlimited@7.2% Efficiency)

A.6 Supplementary Calculation 6: Electrofuels Production from Wind Energy

Critical values

Annual wind energy: 0.053 kWh/m2/day (North Dakota—[41])

4CO2 + 5H2O + 28e — ^ 1C4H10O + 6O2 (Electrofuels cell metabolism)

Electrical efficiency of electrofuels

(1C(@1.5V)/1.5Je-) x (6.24 x 1018 e- /C) x (1 BuOH/28e-)

x(1mol BuOH/6.02 x 1023 BuOH) x (74g BuOH/mol BuOH) x(36.6 x 106 J/1,000g BuOH)

= 66.8%

Land use requirements

(0.053kWh/m2 /day) x (3.6 x 106 J/kWh) x (4,047m2 /ac) x(1 BOE/6.12 x 109 J) x (365 days/yr)

= 46 BOE/ac — yr (Wind Energy Energy)

= 31 BOE/ac — yr (Electrofuels @ 66.8% Efficiency-Wood-Ljungdahl) = 17 BOE/ac — yr (Electrofuels @ 36.0%

Efficiency-3 — hydroxypropionate bicycle or Calvin cycle, anaerobic)

A.7 Supplementary Calculation 7: Electricity Requirements for Fuel Production

Critical values

4CO2 + 5H2O + 28e- ^ 1C4H10O + 6O2 (electrofuels cell metabolism)

Voltage of delivered electricity: 1.5 V [39]

US electricity nameplate capacity: 1.12 TW [15]

US electricity generation: 4.2 x 1015 Wh/yr [46]

US annual petroleum consumption: 7.0 x 109 barrels of petroleum [15]

Electricity required to produce 10,000 BOE/day

(10,000 BOE / day) x (6.12 x 109 J/BOE) x (1kg BuOH/36.6 x 106 J)

x(28mole — /0.074kg BuOH)x(6.02x 1023e-/mole-) x(1C/6.24 x 1018 e-) x (1.5J/1C(@1.5V)) x (1 day/86,400s)

= 1.06GW

Possible fuel production from current US electricity generation (4.2x 1015 Wh/yr) x (10,000 BOE/day/1.06x 109 W)x (1 day/24 h)

= 1.7 x 109 BOE/yr (24% U. S. annual consumption)

Possible Fuel Production from Current US Electricity Capacity ( @ 100% capacity factor)

(1.12 x 1012 W) x (10,000 BOE/day/1.06 x 109 W) x (365days/yr)

= 3.9x 109 BOE/yr (55% U. S. annual consumption)

A.8 Supplementary Calculation 8: Cost of Electrofuels from Electricity

Calculations for the cost of electricity, CO2 , labor, maintenance, taxes, materials, waste, water, and value of O2 coproduct and can be derived from base costs in Table 3, and Supplementary Calculations 1-7. The annual cost of capital is derived by numerically solving for a net present value (NPV) of zero. This can be done assuming: 3-year construction period with constant spending rate, working capital equal to 15% of the total capital investment, 20-year facility life, 5-year MARCS depreciation of capital, 10% interest rate, 2% inflation rate, 50% nameplate utiliza­tion in year 1, 75% in year 2, and 100% in years 3-20, and 10% down time in years 1-20.

A.9 Supplementary Calculation 9: Cost of Electrofuels from Natural Gas

Critical values

4CO2 +14 H2 + O2 ^ 1C4H10O + 9H2O (Electrofuels cell metabolism)

Cost of H2: $1/kg H2 (on-site production; [32])

Cost of CO2: $0/ton (coproduced with H2 effluent)

25% increase in the cost of capital No O2 coproduct

Balance of systems cost: $0.92/GGE

Cost of feedstock

(14mol H2 /mol BuOH) x (2g H2 /mol H2) x (1kg/1,000g) x($1/kg H2) x (1mol BuOH/74g BuOH) x (1,000g/kg) x(1kg BuOH/36.6x 106 J) x (34.2x 106 J/L) x (3.785L/gal)

= $1.34/GGE (H2 and CO2 combined cost contribution)

= $2.25/GGE (overall cost)

A.10 Supplementary Calculation 10: Land Use Requirements for Electrofuels

Critical values

US daily crude oil consumption: 19,148,000 barrels/day [15]

US daily gasoline consumption: 9,034,000 barrels/day [15]

Electrofuels solar efficiency: 13.3% (Supp Calc 4)

Annual solar radiance: 5.7 kWh/m2/day (El Paso, TX — [40])

Land use requirements

(19.148 x 106barrel crude oil/day) x (6.12 x 109 J/barrel crude oil) x(1kWh/3.6 x 106 J) x (1m2 /day/5.7kWh) x (1ac/4,047m2) x(1J solar/0.133J fuel)

= 10.6 x 106 acres = 16,600mi2

= 129mi x 129mi (for total U. S. crude oil production)

(9.034 x 106 barrel gasoline/day) x (42 gal/barrel) x (3.785L/gal) x(34.2 x 106 J/L) x (1kWh/3.6 x 106 J) x (1m2 /day/5.7kWh) x(1ac/4,047m2) x (1J solar/0.133J fuel)

= 4.4 x 106 acres = 6,950mi2 = 83mi x 83mi (for total U. S. gasoline production)

Microalgal biomass can be pre-treated via hydrolysis using the cellulosic enzymes obtained from fungi, protozoa or bacteria. Widely used cellulosic enzymes are cellu — lase from Thrichoderma reseei and cellulase from Aspergillus niger. The cellulase enzyme consists of three main components [63]; (1) 1,4-b-D-glucan glucanohydro — lases (endoglucanases); break down the cellulose crystallinity, (2) 1,4-b-D-glucan cellobiohydrolases and 1,4-b-D-glucan glucanohydrolases (exoglucanases); the exog- lucanases hydrolyse the individual cellulose fibres into simple sugars and cellobiohy — drolases attack the chain ends producing cellobiose, (3) b-D-glucoside glucohydrolases (b-glucosidases); release glucose monomers by hydrolysing the disaccharides and tetrasaccarides of cellulose and form glucose that is ready to be used in the fermenta­tion process. Even though it is a common practice for biomass to be pre-treated prior to enzymatic hydrolysis, microalgal biomass can undergo the hydrolysis process directly without any pre-treatment due to its non-lignin composition. This makes production of bioethanol from microalgal biomass more economical.

Biological catalysts for biodiesel production, including both enzymes and living organisms, are considered to be one of the most promising alternatives for future use in biodiesel production [38, 79]. They can be implemented either in solution or supported (e. g. in biological films or in packed beds). An advantage of enzymes is that they do not require the utilization of nutrients. An advantage of living organisms is that they can be genetically engineered to improve their performance, resilience, and capacity to operate in harsher conditions.

Lipases obtained from different biological sources are examples of enzymes that can be used to perform the transesterification reaction and that have shown a good tolerance to the oil FFA content [95]. Kaieda et al. [52] show that different enzymes have different capacities and report that certain lipases can be used for biodiesel production even if the oil has high water and methanol contents.

Although extensive research has been devoted to this area, the use of bio-catalysts for biodiesel production is still at the laboratory stage [1, 79, 86, 87]. They can be more efficient, selective, require a lower reaction temperature, and produce less side products or wastes, when compared with other types of catalyzed processes, but the reaction rates are much lower than for the conventional process, normally taking several hours (8-12 h) for similar conversions.

Some of the main problems include the difficulty in determining:

• What are the best enzymes or microorganisms to perform the reaction, depend­ing on the feedstocks characteristics and on the impurities that may exist?

• What are the optimum reaction conditions, in particular what are the optimal molar ratio of reactants, solvents to be used, temperature, and water content?

• How the enzymes will be used, if supported or in solution?

• How to recover and reuse the enzymes?

• How to avoid the enzymes’ deactivation, or the living organisms’ death?

Some studies can be found in literature addressing some of the problems listed above. For example, Shimada et al. [87] concluded that the best way to avoid the inhibition or deactivation of enzymes and maintain the enzyme activity for longer periods of time is their stepwise addition to the reaction mixture, in order to main­tain the oil/methanol ratio at certain optimal levels. Although the addition of co-solvents appears in some cases to have a positive effect on the enzyme stability [ 1], there is still some work to be done in order to identify the most adequate solvents and how they influence the ongoing reaction.

There are many different forms of filtration. These include dead-end filtration, microfiltration, pressure filtration, vacuum filtration and TFF. Filtration involves running the algal culture through filters with defined pore characteristics on which the algal cells accumulate, allowing the medium to pass through. The culture runs through the filters until the filter accumulates a thick algae paste [8]. It has been recognised that the use of fil ter presses under pressure or vacuum are effective methods to concentrate microalgal species that are considered to be large in hydro­dynamic size such as Spirulina plantensis. The recovery of small dimensioned algae species such as Dunaliella and Chlorella with size similar to that of bacteria is difficult to perform with pressure or vacuum filtration methods. Recent studies show that TFF and pressure fil tration can be considered as energy-efficient dewatering methods, as they consume optimum amounts of energy when considering the output and initial concentrations of the feedstock [8]. Simple filtration methods such as dead-end filtration are not effective dewatering methods on their own due to issues with back mixing. However, simple filters can be used in conjunction with centrifu­gation to create better separation [15].

Note that only Scope 1 and Scope 2 emissions are considered in this section. Scope

1 emissions refer to the release of GHG as a direct result of an activity or series of activities (including ancillary activities) that constitute the facility. Scope 2 emissions refer to emissions caused by the activity of the facility but in this case the emissions are not directly released from the facility [37]. Even though Scope 2 emissions are not direct emissions from within the system boundary, the activity within the system boundary causes these emissions to occur at another facility; hence, Scope 2 emissions are considered as well. An example of a Scope 2 emission is the emissions from electricity usage. Even though the use of electricity does not directly increase GHG emissions from within the system boundary, it creates GHG emissions at another facility which is the power station. As per the NGER Act, both Scope 1 and Scope

2 emission have to be reported; however, the financial liability of a corporation only rests with Scope 1 emissions as per CPRS. Scope 3 emissions (process unrelated emissions such as administrative and transportation emissions) are not be consid­ered in this study, as there is insufficient information to undertake an accurate analy­sis. All GHGs and energy usage will be converted to tonnes of carbon dioxide equivalent (tonne CO2-e) to enable ease of comparison. It is assumed that the plant is operating at normal conditions when the audit takes place, and all equipments utilise electricity from the grid, unless otherwise stated.

Temperature is known to be a parameter of great importance for the growth of all the microorganisms, since it affects all metabolic activities as well as nutrient availabil­ity and uptake [109].

Vonshak [109] reported for S. platensis cultivation an optimum temperature ranging from 24 to 38°C, depending on the strain. However, satisfactory growth was shown starting at 20°C [ 81] . The usual optimal temperature for cultivation of Spirulina spp. is in the range 35-38°C [109]. However, it must be pointed out that this range of temperature is arbitrary. Many Spirulina strains will differ in their optimal growth temperature, as well as in their sensitivity to extreme ranges [109].

It was previously proposed [28, 98] that cell growth in different bioprocesses can be kinetically described assuming its direct dependence on the activity of one enzyme limiting the overall metabolism. Based on this supposition, the thermody­namics of the system was described resorting to the so-called thermodynamic approach, according to which the specific growth rate increases with temperature would be contrasted by a progressive activity decrease owing to “thermal inactiva­tion” [87]. These authors, who worked with urea as a nitrogen source by a fed-batch process, demonstrated that the best growth temperature was 30°C, which is in accordance with previous results [48, 83]. Besides, Danesi et al. [33] performed S. platensis cultivation at 36.8°C, where there was decline in cell growth, thus sug­gesting possible thermal inactivation of this microorganism, as observed for other microorganisms [37, 90, 98]. Contrary to the use of nitrate as a nitrogen source [109],this cyanobacterium grew reasonably well even at lower temperature (23.3°C) when urea was used as a nitrogen source [87]. It could have happened due to the fact that urea hydrolysis to ammonia has less energy requirements than the reduction of nitrate to ammonia; therefore, the thermal situation at this temperature may be sufficient to sustain nitrogen metabolism. Moreover, when using nitrogen sources that lead to the presence of ammonia in the cultivation medium, the availability of the nitrogen source in the A. platensis cultivation depends on temperature. In fact, when urea is used as a nitrogen source, it can be hydrolyzed to ammonia by urease [93] and/or spontaneous hydrolysis in alkaline medium [33], releasing ammonia, which may be lost by off-gassing [86]. The temperature would then have a dual influence on the culture: besides the fact that higher temperatures can promote microorganism growth, it can also increase the off-gassing of ammonia to the envi­ronment and may lead to a nitrogen limitation in the culture medium. Such fact is particularly important when working with open ponds.

In general terms, the bioactivity of algal and microalgal extracts can be tested using two big groups of techniques: chemical and biological methods. Since no universal method to test bioactivity exists, marine extracts are commonly evaluated by using several methods.

As will be seen in Sect. 4, most of the bioactive compounds that can be found in algae and microalgae have been described to possess antioxidant activity; thus, most of the chemical methods that will be explained in this section are directed to measure different parameters related to the antioxidant activity.

On the other hand, marine compounds have been associated with a high number of bioactivities (mainly pharmacological activities) that can be tested by biological or biochemical methods. In this sense, several reviews covering both general and specific subject areas of marine pharmacology have been published. This kind of review arti­cles has been grouped by Mayer et al. [105] as: (a) general marine pharmacology;

(b) antimicrobial marine pharmacology; (c) cardiovascular pharmacology; (d) antituberculosis, antimalarial, and antifungal marine pharmacology; (e) antiviral marine pharmacology; (f) anti-inflammatory marine pharmacology; (g) nervous system marine pharmacology; and (h) miscellaneous molecular targets.

The OLR, HRT, and SRT are other essential characteristics of ADP. The rapid increase of OLR, especially of readily digestible substrate, causes fast acid forma­tion, leads to alkalinity depletion and a drop in pH. The HRT determines the volume and capital cost for an AD system. The SRT influences the volatile solids (VS) reduction and, thus the methane yield from biomass. Significant fluctuations in OLR, HRT, and SRT lead to upset of the ADP and inhibition of the methane production.

1.1.2 Toxicants

Hydrolytic, fermenting, and acidifying organisms are tolerant to the presence of oxygen but methanogens are strict anaerobes. Oxygen concentration as low as 0.1 mg/L starts to inhibit the production of methane.

High salts concentration (e. g., NaCl) can affect methane production when marine algae are used for anaerobic digestion. Nevertheless, the methane yield from green macroalgae diluted with seawater was comparable to the methane yield from a sam­ple diluted by fresh water [77] . A shock increase in salt concentration in a fixed bacteria reactor caused inhibition only at 35 g/L. When the NaCl concentration was increased gradually, methanogens adapted to concentrations up to 65 g/L [ 78]. Moreover, desalination of macroalgae by heat and pressure resulted in less methane yield compared to untreated algae likely because of the loss of easily digestible organic matter [77, 79].

Heavy metals, such as lead, cadmium, copper, zinc, nickel, and chromium, are well-known toxicants for bacteria. Some algae accumulate heavy metals but their negative effect can be decreased by precipitation with sulfide compounds.

By-products, including ammonia and hydrogen sulfide, at high concentrations can be toxic for methanogenic microorganisms [62]. Generally, the main source of nitrogen and sulfur in AD is proteins, but some seaweeds have a high amount of sulfated carbohydrates. The toxicity of ammonia and sulfide is related to the pres­ence of metals, temperature, and pH in digesters since neutral forms of ammonia and hydrogen sulfide are more toxic, possibly because they can more rapidly pene­trate the cell membrane [65, 80-82]. On the other hand, other authors have reported increasing sulfide toxicity with increasing pH [83]. This discrepancy is possibly due to different mechanisms of sulfide toxicity on different species. The mechanism of sulfide toxicity is usually associated with the following factors: sulfate reducing bacteria that are able to outcompete methanogens for hydrogen and acetate [84]; denaturation of native proteins through the formation of sulfide and disulfide cross­linkage between polypeptide chains [85]; interference with the assimilatory metabolism of sulfur [86]; and the ability to remove essential metals (nickel, iron, cobalt) from the solution.

The mechanisms of ammonia toxicity are possibly associated with disruption of intracellular pH, potassium deficiency, and inhibition of a specific enzymatic reac­tion [87,88]. Several studies showed that ammonia is toxic for methanogenic micro­organisms at concentration 1.5-1.7 g N/L at pH 7.4 and above [89, 90]. Whereas methanogens tolerate ammonia concentration up to 3-4 g N/L at lower pH [90-92]. Moreover, microorganisms are able to acclimate to high ammonia concentration, and ADP can be stable at nitrogen concentrations as high as 5-7 g/L [93-96] .

Organic acids are common intermediate products of AD but accumulation of them, especially in nonionic form, inhibits the overall process. Decline of hydrogen utilization causes accumulation of propionate, leading to failure of the acetoclastic methanogenesis, and therefore causing acetate accumulation and a drop in pH [97]. The mechanism of inhibition by organic acids is probably the denaturation of cell proteins.