Achievements and perspectives of anaerobic co

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Journal of Cleaner Production 194 (2018) 359e371

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Achievements and perspectives of anaerobic co-digestion: A review Md. Nurul Islam Siddique*, Zularisam Ab. Wahid Faculty of Engineering Technology, University Malaysia Pahang (UMP), Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, Malaysia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 January 2018 Received in revised form 17 May 2018 Accepted 19 May 2018 Available online 20 May 2018

The world is now seeking sources of renewable energy that are both economical and environmentally friendly. Purified biogas is one essential source of renewable energy that can act as a substitute for fossil fuels. Anaerobic digestion has been recognised as a biochemical method of biogas generation that can convert organic compounds into a sustainable source of energy. Anaerobic co-digestion, AcoD is considered a pragmatic method to resolve the difficulties related to substrate properties and system optimisation in single-substrate digestion processes. The present manuscript studied the research prospects and challenges of anaerobic co-digestion, and the contributions of different methods in biogas generation studies. With the increased use of anaerobic co-digestion, the complexity of the process also increases. Several mathematical models had been established to optimise the anaerobic co-digestion technique. The biological methane potential test is a preferred technique for measuring the biodegradability and decomposition rate of organic substances. Furthermore, various additives are now used to maximise methane production. The improvement and optimisation processes of biogas production still need to be investigated in greater detail. In developing countries like Malaysia, biogas production may be more economically feasible if the latest simulation and characterisation methods are used at the industrial scale. Finally, this review describes a design and development framework to incorporate various aspects to enhance biogas production. © 2018 Elsevier Ltd. All rights reserved.

^ as de Handling Editor: Cecilia Maria Villas Bo Almeida Keywords: Anaerobic co-digestion Biogas generation Biochemical methane potential Biodigestibility Modeling

Contents 1. 2. 3.

4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 Aims and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Factors affecting AcoD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 3.1. Chemical properties of the wastewaters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 3.2. Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 3.3. pH value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 3.4. Particle size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 3.5. Carbon to nitrogen ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 3.6. Organic loading rate (OLR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 3.7. Hydraulic retention time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 Progress and challenges pertaining to the AcoD process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 4.1. Use of additives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 4.2. Pretreatment and post-treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 4.3. Biochemical methane potential (BMP) tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 4.4. Two-phase anaerobic co-digestion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 4.5. Anaerobic digestion and biogas production from algal biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 Mathematical modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 5.1. Basic kinetic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 5.2. Anaerobic digestion model No.1 (ADM1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366

* Corresponding author. E-mail address: [email protected] (Md.N.I. Siddique). https://doi.org/10.1016/j.jclepro.2018.05.155 0959-6526/© 2018 Elsevier Ltd. All rights reserved.

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6. 7. 8.

5.3. Statistical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 5.4. Computational fluid dynamics (CFD) models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 AcoD process for biogas generation in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Future prospects for the AcoD technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369

Nomenclature AcoD AD ADM1 ANN BMP C/N CaO CCD CFD CM CS

Anaerobic co-digestion Anaerobic digestion Anaerobic digestion model No 1 Artificial neural network Biochemical methane potential Carbon to Nitrogen ratio Calcium oxide Central composite design Computational fluid dynamics Cattle manure Corn stover

1. Introduction Energy insecurity and environmental contamination are perhaps the greatest challenges mankind has ever faced in this century. Alleviation of carbon dioxide (CO2) discharge and associated global warming drive the search for sources of energy alternatives to fossil fuels. At present, bioenergy utilisation is the best option for economic development and improving quality of life in developing countries as biomass is a potent source of renewable energy. Anaerobic co-digestion is expected to have a dynamic part in the quest for renewable energy generation (Ebner et al., 2015). AD is a complex and resposive system that depends on various microbs with various operating conditions. It also depends on the type and structure of the substrates. Carbohydrates, lipids, and proteins are the main constituents of organic materials that can be degraded into a simpler form by the microbes in an oxygen free environment. In general, biogas is made of methane and CO2. The AD system consists of the following stages: hydrolysis, acidogenesis, acetogenesis, and methanogenesis (Deublein and Steinhauser, 2011). Single-stage digestion may lead to reactor failure with less methane generation as the actions of methanogens might be hindered below pH 6.79 whereas acidogens are functioning at pH 5e6 (Sinpaisansomboon et al., 2007). To resolve this issue two-stage digesters are introduced. In two-stage digestion the culture of acidogens and methanogens are performed in separate digesters. Usually the initial phase digestion is operated at pH (5e6) and short hydraulic retention times of 1e3 days while the second phase is run at pH (6.79e7.19) and prolonged HRT of 14e28 days (Muha et al., 2013). Anaerobic co-digestion can be considered as the instantaneous digestion of two or more substrate and co-substrate mixtures. A schematic diagram of the AcoD process was presented in Fig. 1. Normally, AD processes are designed for a single substrate. However, using a variety of substrates makes the process more stable. Many researchers have been eager to investigate co-digestion using various mixtures of industrial, farming, agricultural, and municipal waste materials (Tasnim et al., 2017). The primary concern for the

CSTR FW LCFA NP OFMSW OLR RSM SCMD SS VFA VS WAS

Continuously stirred tank reactor Food Waste Long-chain fatty acid Nanoparticles Organic fraction municipal solid waste Organic loading rate Response surface methodology Simplex-centroid mixture design Sewage sludge Volatile fatty acids Volatile solid Waste activated sludge

AcoD process is improving biogas and methane generation. Besides, AcoD can improve process stabilisation, nutrient balance, and the synergistic effects of microorganisms, and can reduce greenhouse gas emissions and processing costs (Henard et al., 2017). A few key features, comprising co-substrate characteristics, co-substrate prompted inhibitions, and organic loading rate, may grately influence the AcoD system (Xie et al., 2016). When the co-substrate is selected, shipping costs from the point of generation to the AcoD plant should be considered (Mata-Alvarez et al., 2014). Moreover, given the aim of favouring synergy and optimising methane production, selecting an appropriate co-substrate and mixing proportion is also essential. The AcoD technology employs different feed wastes for increasing methane generation (Table 1). Co-digestion of Potato waste and Pig waste generates the maximum methane of while codigestion of Cow manure and chicken waste yields minimum methane under mesophilic state. On the other hand, improper choice of co-substrates, compositions, and operational states may lead to system imbalance and reduce methane generation. Hence, a comprehensive AcoD mathematical model is obligatory for laboratory-scale research and full-scale design and operation. Mathematical modeling of the AcoD process can forecast the impacts of the mixing proportion of two or more co-substrates, organic loadings, and the choosing technique of wastewaters, and can minimise energy usage and time of the procedure (Poggio et al., 2016). Thus, basic kinetic models, AD model No. 1, and other algorithmic methods have been developed (Xie et al., 2016). The dramatic increase in publications about the AcoD process (Fig. 2) in the last decade reflects the viability and suitability of AcoD process in the improvement of biogas generation and ecological sustainability. The feedstock and environmental conditions must be adjusted for designing universal digesters and optimising the AcoD process (Mata-Alvarez et al., 2014). Yet, insightful investigation of factors affecting anaerobic co-digestion, adjustment of operating parameters and optimisation strategies are still vague. Therefore, this manuscript aims to deliver a brief, up-to-date overview of the use of additives in AcoD, pretreatment, biochemical

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Organic Substrate B

+

Organic Substrate A

361

Pre and PostTreatment

Proteins M a t h e m a t i c a l M o d e l i n g

Carbohydrates

Lipids

Hydrolysis Amino Acids Sugars

P

Acidogenesis Intermediary Products (Propionate, Butyrate) Acitogenesis

Acetate

Hydrogen

Methanogenesis Methane

Fig. 1. Flow Diagram of Anaerobic Co-digestion and modeling application.

Table 1 Comparison of Anaerobic co-digestion of various substrates and methane yields (Callaghan et al., 1999; Monou et al., 2008). Substrate

Condition ( C)

Methane yield (L/g VSadded)

Potato waste and Pig waste Cow manure and Fish waste Cow manure and Brewery waste Cow manure and yogurt waste Potato waste and Abattoir waste Cow manure and fruit and vegetable wastes Cow manure and chicken waste

Mesophilic Mesophilic Mesophilic Mesophilic Mesophilic Mesophilic Mesophilic

0.42 0.36 0.30 0.26 0.24 0.22 0.13

methane potential tests, anaerobic digestion and biogas production from algal biomass and two-stage anaerobic co-digestion processes. The system optimisation and enhancement of methane generation still need deeper studies (Hagos et al., 2017). In addition, application of mathametical modeling approaches in AcoD process may speed up the revolution to industrializations (Xie et al., 2016). In addition, it can attain the remarkable enhancement of methane generation as a renewable energy in developing countries, such as: Malaysia. For deeper understanding of the application and performance of mathametical model, we performed a review of the literature to demonstrate CFD, statistical models and ADM1 model applications on optimisation of methane production. This article presents the current status and perspectives on the biogas production process, and recommends some practical steps for further development of AcoD systems. 2. Aims and methods The aim of this manuscript is to deliver a short but comprehensive overview of the AcoD process of biogas production.

(37) (37) (37) (37) (37) (37) (37)

Consequently, we aim to summarise the research to recognise fields where the AcoD process has been effectively embraced, and where it has not been possible to do so, and to identify the difficulties emerging out of utilisation of AcoD processes. Further, we report on previous studies and various alternative explanations presented by scientists in this field. Subsequently, we analyse these studies, identify gaps in expertise and issues that remain to be solved, and suggest directions for further research. The strategy for a logical and orderly review utilised here has been described in detail by Fink (2005), and additionally elaborated upon by Denyer and Tranfield (2009). They explain that an audit of this kind should have following properties. 1. Replicable: The audit should be completed using a wellcharacterised system with no predispositions, so that the result is reproducible. 2. Exclusive: The study should be based on the latest and best available evidence, so as to provide the least biased answer to the question at hand.

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3000

Number of Papers

2500 2000 1500 1000 500 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Publication Year Fig. 2. Evaluation of number of papers published from 2007 to 2016 with titled of Anaerobic Co-digestion (https://login.webofknowledge.com/).

3. Aggregative: The results of various examinations should be consistent so that the outcome is reliable. 4. Algorithmic: The survey should incorporate insights into the investigative conditions or calculations used by the analysts to measure the correct parameters under which the outcomes or results were pertinent. The survey is directed as follows: 1. Question formulation: Define the focus of the survey and the plan to address it appropriately. 2. Finding studies: Locate and gather as much research as could be expected in the area of interest. 3. Study determination and evaluation: Information selection, consideration, and rejection criteria must be characterised explicitly. 4. Analysis and synthesis: After the needed information is gathered, individual examination should be conducted to separate it into its logical constituents, and the data should then be incorporated as required. 5. Revealing and utilising the outcomes: The data gathered following the method described above should then be utilised to help determine outcomes, conclusions, and future directions. Journals listed on the Science Direct, Scopus, Springer, Emerald and Elsevier websites were searched for unique research articles and review articles in the fields cited above for the literature survey. The period of time considered for the literature survey was the last 17 years. The wide variety of papers published over 10 years are shown in Fig. 2.

3. Factors affecting AcoD The optimum performance of AD depends on several parameters. For the optimisation of the co-digestion system methane generation technique, the chemical properties of wastewater, functioning parameters (temperature, pH, Particle sizes, C/N ratio, OLR and HRT etc), BMP tests will be the key parameters. In addition, stable conditions are required to maximise the performance of microbes for biogas production inside the reactor. Application of mathematical models in conjunction with these parameters can

maximise the methane generation from co-digestion system.

3.1. Chemical properties of the wastewaters Understanding the chemical properties of various wastewaters is useful to select the suitable wastewaters for AcoD. Chemical compositions of substrates depend on their sources, although most of the time it is hard to know the exact composition. Grouping substrates by biochemical composition may be useful to asses their biodigestibility, and bioavailability. Carbohydrates are a common component of all substrates. Food waste (FW) is highly enriched in various sugars, which are decomposed through acidogenesis to form volatile fatty acid (VFA). High concentrations of sugars can accelerate VFA concentration in the digester and decrease pH. All plant-derived substrates are rich in carbohydrates. Despite being difficult to degrades, cellulose shows a great potential for biogas production. Starch is a very common substance found in rice, pasta, and potatoes. It is degraded quite easily in the biogas generation system. The AcoD technique may increase the degradebility of cellulose and hemicelluloses, as well as the buffering capacity of ammonia and VFA (Zhou et al., 2014). Organic wastes such as abattoir waste, farmhouse waste, and waste from the ethanol industry have huge protein concentration. Domestic wastewater and FW carry lower amounts protein. Protein-rich substrates can produce relatively large amounts of methane in the biogas process (Murovec et al., 2015). When proteins degrade, ammonium ions are released. These ammonium ions are potent inhibitors of methanogenic microbes. Ammonia can also inhibit the activities of microbes when exist in high concentrations (Schnürer and Nordberg, 2008). Selecting suitable co-substrates and an appropriate C/N ratio can minimise this problem. Highfat-content organic materials are used for high biogas production, as they are readily degradable. However, high lipid concentrations can create problems such as blockages, adsorption into biomass, and microbial inhibition in anaerobic digesters. Triglycerides degrade into long-chain fatty acids (LCFAs) and glycerol. LCFAs may hinder the activity of anaerobic microorganisms and make this process more unstable (Salakkam and Webb, 2015). In addition, at high-temperatures, LCFAs can cause foaming (Nguyen et al., 2017). For proper nutrition balance and microorganism enrichment, carbohydrate-rich materials are mixed with fat-rich materials,

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supplementing additional alkali chemicals, such as and CaCO3, is recommended (Siddique et al., 2015a,b).

which enhance the rate of biogas generation. 3.2. Temperature

3.4. Particle size Choosing and controlling the temperature are critical as it drives the function of microorganisms in the process of AD. There are three different temperature ranges at which AD can be carried out: psychrophilic (25  C), thermophilic (approximately 55  C), and  ska and Karwowska, 2017). mesophilic (approximately 35  C) (Rosin Among them, mesophilic and thermophilic conditions are standard practice. The mesophilic process is more stable compared to the thermophilic process because a wider variety of microorganisms favour mesophilic temperatures to thermophilic temperatures (Yang et al., 2018). Furthermore, high concentrations of ammonia make the thermophilic stage unstable (Nkemka and Hao, 2018). Maintaining a constant temperature required stabilisation of the digestion process (Sun et al., 2016). Although the gas production rate rises with increased temperature, it reduces the content of methane (Dai et al., 2017). The most continuous and stable production of methane can be attained at 32e35  C (Sorathia et al., 2012). With the increase of temperature, the solubility of CO2 decreases. In mesophilic digesters, with lower temperatures than thermophilic digesters, CO2 can quickly dissolve and produce carbonic acid by reacting with water, thus increasing the acidity. Therefore, it can be concluded that temperature was the essential parameter for the growth of microbs, and thus for biogas generation. 3.3. pH value The pH concentration has an importent influence in the AD system, as it affects the solubilisation of organic matters (Feng et al., 2015). It can indicate a favourable atmosphere for the microbes of the reactor (Dai et al., 2015). The enzymatic reactions of microorganisms depend on pH (Neshat et al., 2017). The pH of the digester affects fermentation significantly to produce biogas. In the biogas production process, different microorganisms require different optimal pH values, although most of them prefer neutral pH conditions. For maximum methane yield, many researchers (Lemmer et al., 2017) have noted that maintaining a pH in between 6.8 and 7.2 is preferable. Hydrolysing and acidogenic microorganisms prefer a pH values within the range of 5.5e6.5 (Kusch et al., 2011). However, the optimal pH for methanogenic microorganism is near 7.0 (Yao et al., 2017). The optimal pH concentartion was the key reason to devide some reactors into two phases, with an acidogenic and methanogenic phase (Garoma and Pappaterra, 2018). The production of VFAs in the initial stages of digestion lowers the pH in the digester and inhibits the methanogenic activity of microorganism (Anggarini et al., 2015). To overcome this problem,

Particle sizes also affect the AcoD process of biogas production. Larger particles may cause clogging and make the digestion process more difficult. In contrast, smaller particle size enhances the specific surface area, that helps microorganism to work more swiftly in the hydrolysis step (Vigueras-Carmona et al., 2016). Larger dry solids in digestible silage (>3 cm) caused major operational problems (Wall et al., 2015). Applying two-phase digestion systems is one feasible solution to minimise this problem (Wall et al., 2016). Agyeman and Tao (2014) increased the rate of methane production by 10e29% by decreasing the FW particle size from 8 to 2.5 mm. Silvestre et al. (2015) achieved exceptional results in co-digestion of sewage sludge (SS) and organic fraction municipal solid waste (OFMSW). They could not detect any significant change in methane yield by reducing the size of OFMSW from 20 to 8 mm. 3.5. Carbon to nitrogen ratio The carbon to nitrogen (C/N) ratio of organic materials effects the entire AcoD process (Reilly et al., 2016). Substrates with an optimal C/N ratio provide sufficient nutrients for microorganisms to maximise biogas production. Lower C/N values leads to higher concentrations of ammonia and impede microbial growth. When the C/N ratio is greater than the optimal value in the fermentation process, large amounts of VFAs are produced. Thus, maintaining an appropriate C/N ratio is important in the AcoD technique of biogas generation. The main limitation of cattle by-products (Table 2) was the lack of nutrient concentrations, mainly the low C/N ratio, which reduces microbial activity. Organic wastes used in biogas production are generally rich in lignocellulose-type resistant materials. Thus, special pretreatments are required to utilise such wastes in short retention times with anaerobic organisms (Kabir et al., 2015). Therefore, significant amounts of “invalid carbon” in these materials affect the calculation of C/N. Thus, the present C/N calculation shows only the general characteristics of organic waste materials, not the actual substances utilised by anaerobic microbes. The optimal C/N ratios of various substrates attained from different AD process will likely be different. The AD process is more stable when the C/N ratio ranges from 20 to 30. During AcoD process, cosubstrates are added to maintain the C/N proportion in digesters (Moset et al., 2017). To unify a specific range, including the existing carbon of the easily degradable part, and excluding the carbon that is not specifically affected by microorganisms, an available carbon/ nitrogen ratio is proposed (Wang et al., 2017c). By maintaining the C/N ratio at 17/1, Dai et al. (2015) enhanced methane generation by

Table 2 List of substrates characterised into lower and higher value of C/N ratio (Guillaume and Lendormi, 2015). Comparatively lower C/N value materials

C/N ratio

Comparatively higher C/N value materials

C/N ratio

Cattle manure Poultry manure Pig manure Sheep manure Horse dung Kitchen waste Vegetable wastes Food wastes Peanut waste Grass cutting wastes Slaughterhouse waste Goat manure

15e26 4e16 7e15 20e34 19e26 26e30 8e36 2e18 19e32 11e15 21e36 9e18

Rice straw Wheat straw Sugar cane waste Corn waste Oats straw Algae Sawdust

50e68 51e151 139e151 51e57 47e51 74e101 199e501

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around 3.8- and1.5- fold compared with perennial ryegrass alone, and waste activated sludge (WAS) alone, respectively.

microalgae and activated sludge. Within nine days of incubation, they observed rapid biogas production.

3.6. Organic loading rate (OLR)

4. Progress and challenges pertaining to the AcoD process

The organic loading rate may be considered as the amount of dry organic solids loaded per unit time, per unit volume of a digestion process. OLR is a key parameter for optimal microorganism activity (Neshat et al., 2017). Lower OLR leads to inefficiency of AD technology and vice versa (Li et al., 2015b). Higher OLR enhances different microbial species, requires less energy for heating (Nagao et al., 2012), and reduces the necessary digester size and cost (Chandra et al., 2012). However, when the OLR is increased beyond a specific range, it higher accumulation of VFA and ethanol, poor heat transfer, and uneven distribution during stirring. Higher OLR can damage the circulating pump when it crosses the carrying capacity of the pump (Rico et al., 2015). Different optimal OLRs are obtained from the various AD process of organic wastes. Paudel et al. (2017) reported that an OLR of 1.24 g VS L1 d1 was optimal for methane production and organic removal in a two-stage continuously stirred tank reactor ran at mesophilic conditions (37  C). Continuous AD of Spirulina platensis was performed by Aramrueang et al. (2016) with varying OLR. OLR of 1.0 [g VS] l1 d1 produced the maximum methane yield of 0.342 l [g VS]1. Using a semi-continuous digester, Li (2016) found that 1.88 g VS/day was the optimum OLR for the combined degradation of grass and horse waste.

AcoD is an efficient method for biogas production, as well as for environmental protection. Nutritional imbalance and complex lignocellulosic structures make it difficult to direct utilisation of organic materials. Organic materials should achieve the nutritional requisites for microorganism growth in the digestion system of biogas generation. The C/N ratio affects the decomposition of organic materials. The co-digestion of nitrogen-rich wastewaters with carbon-rich biomass could estabilize the C/N ratio and improve the growth rate of microorganisms (Nugraha and Matin, 2017). The current progress of co-digestion with different substrates and comparison of biogas yield is shown in Table 3. In addition, the coexistence of unlike organic wastes in the same geographical place facilitates integrated management, providing substantial ecological welfare, such as energy conservation, recycling, and minimization of CO2 emission.

3.7. Hydraulic retention time The amount of time needed for any microorganism to consume and synthesise the substrate is called the hydraulic retention time (HRT). Uncontrolled HRT inhibits the metabolic activity of microorganisms. Long HRTs can lead to the death of microorganisms because of shortage of nutrients. For industrial-scale applications, a short HRT is recommended to decrease the volume of the digester and the investment costs (Li et al., 2015b), and to maximise biogas production (Grosser, 2017) and net electrical energy production (Di Maria et al., 2015). Adding water to the substrate can reduce the HRT. However, if the HRT is shorter than the generation times of the microbes, it causes the washout of microbes, and thus leads to failure of the AD system (Dareioti and Kornaros, 2015). Xie et al. (2017) found that concentration of VFAs and alkalinity increased with increasing HRT and vice versa. They also found that depending upon the desirable temperature limit and nature of microbs, the optimal HRT may vary for different co-digestion processes. Dareioti and Kornaros (2015) reported that a highest methane generation rate of 0.90 L/LR d was achieved at a HRT of 16 days. Anbalagan et al. (2016) investigated the influence of HRT on settleability, nutrient removal, and biogas generation with the integration of freshwater

4.1. Use of additives AD is a universal technology for production of methane as a renewable energy source. The application of inorganic and biological additives can improve the applicability of the process. Nanoparticles (NPs) are now commonly used in commercial products for industrial-scale applications. Consequently, the impact of NPs on AcoD processes is raising concerns. The effects of metal oxide (i.e. TiO, SiO2, Al2O3, and ZnO) NPs on AD of WAS is studied by Mu et al. (2011). However, the authors found that there was no beneficial or inhibitory effect of TiO2, Al2O3, or SiO2 on biogas production. Moreover, the addition of 30 and 150 mg g1 TSS1 of ZnO caused 23% and 81% reduction of biogas production, respectively. The adverse effects of ZnO and CuO NPs on methanogens can be mitigated by adding sulfide (Gonzalez-Estrella et al., 2015). Adding 3 g of magnetite and 1 g of natural zeolite to wheat straw, chicken manure, and sheep manure co-digestion caused maximum increments of methane production of 52.01% and 51.01%, respectively at a mesophilic temperature (35  C) (Liu et al., 2015). Abdelsalam et al. (2017b) added spherical Co and Ni NPs about 17e28 nm in diameter to raw manure to improve biogas and methane at 37 ± 0.3  C. Microorganisms and enzymes are commonly used as suitable alternatives to physicochemical pretreatments of substrates before the digestion process (Parawira, 2012). Biological additives stimulatie microbial activity to improve methane production (Abdelsalam et al., 2017a). The inoculum helps to increase methane production by 53% compared with the non-inoculated controls

Table 3 Current progress in co-digestion with different substrates. Feed stock

Condition

Corn straw and blue micro algal biomass

Mesophilic

Comparison of biogas yield

Co-digestion of these two substrates at carbon to nitrogen proportion of 21:1 enhanced the biogas generation remarkably, producing a specific methane yield of 235 mL/g VS at an OLR of 7 g VS/L/d. A Methane generation rate of 1403 mL/L/d was attained by co-digestion that was 45% more than individual-digestion. Manure and olive mill waste Thermophilic When olive mill waste was co-digested with and cattle manure at a carbon to nitrogen proportion of 20:1, it enhanced the methane production up to 17%, producing approximately 178 mL CH4/gVSadded. Petrochemical wastewater Mesophilic When Petrochemical wastewater was co-digested with and cattle manure at a carbon to nitrogen proportion and cattle manure of 22:1, it enhanced the methane production up to 50% without VFA accumulation. Arthrospira platensis and Mesophilic Co-digestion of A. platensis and carbon-rich feed stock such as barley straw, at a carbon to nitrogen proportion barley straw of 24 enhanced biogas production than the individual-digestion. A methane production rate of 324 mL/g VS at an OLR of 2 g VS/L/d, was achieved by co-digestion that was 16% more than individual-digestion. Olive mill wastewater and Mesophilic Co-digestion of Olive mill wastewater and poultry manure with a mixing ratio of 25:75 and at a carbon to poultry manure nitrogen proportion of 22:1 increased biogas generation by 20e25%.

References (Zhong et al., 2013) (Goberna et al., 2010) (Siddique et al., 2014) (Herrmann et al., 2016) (Gelegenis et al., 2007)

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(Romero-Güiza et al., 2016). Enzymes can be directly dosed into the digester (Parawira, 2012) or used for pretreatment of complex organic substrates (Christy et al., 2014). Inoculation with lipases in AcoD of SS and grease trap improved methane production by 87 mL CH4 g VS1 (Donoso-Bravo and Fdz-Polanco, 2013). 4.2. Pretreatment and post-treatment The complex structures of cellulose, hemicellulose, and lignin cannot be degraded easily by microorganisms (Jaffar et al., 2016). Pretreatment is required to transform these substances into biodegradable compounds that can be easily consumed by microorganisms (Abudi et al., 2016). Pretreatment is used to increase chemical oxygen demand and release the intracellular nutrients of the substrates and enhance methane generation (Neshat et al., 2017). -Bundo  et al. (2017) conducted thermo-alkaline pretreatSole ment of microalgal biomass and wheat straw by adding 10% CaO before putting the material in the oven (72  C) for 24 h. Pretreatment enhanced the methane yield by 15% compared with untreated substrates. Thermo-alkaline pretreatment of hulls of A. hypogaea was carried out using 3 g NaOH and 100 g TS at 55  C for a 24 h period (Dahunsi et al., 2017). They observed 66.15% higher experimental biogas yield using the thermo-alkaline pretreated hulls relative to the untreated hulls. Ultrasonic pretreatment degrades high polymeric material to enlarge the reaction boundary of the substrate at high temperature. Using ultrasonic pretreatment, Siddique et al. (2017) increased methane yields from co-digestion of petrochemical wastewater with WAS by 25%. An ultrasonic homogeniser was used for 30 min at the energy intensity of 360 kJ/L to co-digest FW and WAS and increased biogas generation by 56.2% relative to the control (Naran et al., 2016). Microwave (MW) irradiation has also been used to enhance the degradability of substrate and increase biogas yield (Wang and Li, 2016). An experiment carried out by Siddique et al. (2017) reported that the production of biogas enhanced significantly (by 53%) after microwave pre-treatment of WAS. A recently published article reported that microwave pretreatment before sludge digestion enhanced biogas generation by 45e79% (Zhen et al., 2017). Chemical pretreatment is commonly used to break down the bonds of lignocellulosic materials (Siddique and Zularisam, 2012). Using NaOH, KOH, and other substances as alkali pretreatment is an effective chemical pretreatment method. Mel et al. (2015) soaked corn husks in NaOH solution at room temperature for five days before co-digestion with cow dung. They reported increased methane content from 60% to 80%. Use of ammonia solution is one the most efficient ways to lower the lignin content of lignocellulosic materials (Jurado et al., 2016).

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Although biological treatment is sensitive to environmental factors and slow, it consumes less energy, does not produce or require any toxic chemicals, and is environmentally friendly. Hydrolytic enzymes and fungi are commonly used to enhance the degradation of lignocellulosic materials (Tisma, 2017). Approximately twice as much methane can be generated after pretreating the chicken feathers with Bacillus sp. C4 compared with untreated  ski and Kowalska-Wentel feathers (Patinvoh et al., 2016). Ziemin (2015) reported 28% higher biogas yield of hydrolytic enzymes pre-treated sugar beet pulp silage and vinasse compared to untreated material with same mixing ratio. Microbial fuel cells has the capacity to digest the residual organic materials in the anaerobically digested waste (Frattini et al., 2016). In addition, MFC can further recover energy from effluent. Application of MFC may upgrade the quality of the effluent costeffectively than the aerobic process (Soltan et al., 2017). It can improve the sustainability of the treatment system (Mohan and Chandrasekhar, 2011). Finally, the integration of post Microbial fuel cells treatment to the anaerobic digestion system can enhance both energy recovery and pollution control from substrates (Tugtas et al., 2013). 4.3. Biochemical methane potential (BMP) tests Inexpensive and repeatable biochemical methane potential tests have been widely used to asses biodigestibility, methane yield, reaction-rate kinetics, the extent of anaerobic activity, the influence of inoculum pre-treatments, and the effects of mixing with different viscosities (Wang et al., 2017a). A number of studies have established and demonstrated BMP tests, such as with municipal solid waste leachate, pulp and paper mill sludge, organic household waste, commercial FW, and livestock and agri-FW streams (Hidalgo and Martín-Marroquín, 2015). BMP helps to determine the biodegradability and bio-availability of wastewaters (Pecorini et al., 2016). They also reported that the information determined by BMP tests is helpful to characterise and evaluate the optimal design and performance of the AcoD process. In addition, BMP testing can reveal the possible mechanisms of synergy between the codigestion mixtures (Ebner et al., 2016). Table 4 shows BMP application in co-digestion and comparison in methane production. The conventional BMP process is complex and time-consuming. Generally, it takes 30e90 days or longer. This length may increase the cost of feedstock storage and management, and the optimal combinations of substrates may be unstable. Researchers have suggested various alternative methods to alleviate the drawbacks of traditional BMP measurement. Naroznova et al. (2016) proposed a chemical component composition (carbohydrates, lipids, and proteins)-based BMP model of different biomasses. For the BMP forecasting of municipal organic wastes, Fitamo et al. (2017)

Table 4 BMP application in co-digestion and comparison in methane production. Feed stock

Condition

Sewage sludge and organic fraction Mesophilic of municipal solid waste

BMP comparison of biogas yield

Co-digestion of these two substrates at a proportion of 75:25 enhanced the biogas generation remarkably. A 37% increment in Methane generation rate of was attained by co-digestion than the individual-digestion of Sewage sludge. Kitchen waste and thickened sludge Thermophilic When kitchen waste was co-digested with thickened sludge at a proportion of 40:60, it enhanced the methane production up to 39.65%, producing approximately 2.3 l CH4/L/d. Waste activated sludge and Mesophilic When waste activated sludge was co-digested with and Synthetic kitchen waste at substrate to Synthetic kitchen waste inoculum proportion of 1.2, it enhanced the methane production up to 63.89%. Sewage sludge and Food waste Mesophilic Co-digestion of Sewage sludge and Food waste, at a mixing proportion of 1:1 enhanced biogas production than the individual-digestion. A methane production rate of 0.31 L/g VS, was achieved by co-digestion that was 32.25% more than individual-digestion. Waste activated sludge and Food Mesophilic Co-digestion of Waste activated sludge and Food waste with a mixing ratio of 80:20 increased methane waste generation by 47% than the individual digestion.

References (Sosnowski et al., 2008) (Kim et al., 2011) (Li et al., 2011) (Kim et al., 2003) (Heo et al., 2004)

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developed near-infrared-based reflectance spectroscopy. 4.4. Two-phase anaerobic co-digestion The AcoD system comprises four steps: hydrolysis, acidogenesis, acetogenesis, and methanogenesis (Siddique et al., 2015a,b). In a one-stage reactor, it is hard to improve the overall co-digestion system because of the different metabolic characteristics, nutritional prerequisites, growth rates, and optimal operational features for these steps (Hubenov et al., 2015). A two-stage system is used to minimise the obstacles that arise in a one-stage process (Muha et al., 2013). Based on the growth rates and environmental responses of the microorganism, the whole system can be separated into two parts: the acidogenesis and methanogenesis stages. Initially, the concept of two-phase AcoD was designed and developed by Pohland and Ghosh (1971). A two-phase system has several benefits over single-phase reactors: process stability, higher energy recovery, higher biogas production (Krishnan et al., 2016), reduced lag phase, higher VS removal efficiency, and higher energy recovery (Fu et al., 2017). Hidalgo et al. (2015) recommended a two-stage AcoD process over a single stage when organic wastes contain more lipids. Zahedi et al. (2016) showed that Clostridium sporogenes and Methanobacteriales are the dominant microorganisms in the first phase and the second phase, respectively. This distinction helps to avoid the influence of overloading in the second digester during the methanogenesis step. In addition, hydrogen is produced from organic waste VFAs, lactic acid, and alcohol in the first stage, which stabilise the food supply of organic waste. Kanchanasuta and Sillaparassamee (2017) conducted two-stage AcoD of hydrogenic effluent from decanter cake and crude glycerol in mesophilic conditions for methane production. Productivity was improved by a factor of 1.6 compared with the single-stage alternative (Kanchanasuta and Sillaparassamee, 2017). Using Agave tequilana bagasse in the two-stage digestion process, Arreola-Vargas et al. (2016) found the highest biogas yield at concentrations of 40% and 20% in acidogenesis and methanogenic phases, respectively. During the methane production process, two-stage AD reduced the lag phase by 9.1 days compared with the one-stage process (Arreola-Vargas et al., 2016). However, the two-stage reactor has some disadvantages: the inhibition of acid-forming bacteria, technical complexity, and higher initial cost (Wang and Zhao, 2009). To resolve the high operation cost and technical complexity of the two-phase AcoD process, further investigation is still needed. 4.5. Anaerobic digestion and biogas production from algal biomass Microalgae have ample carbohydrates (6.99e70% of VS), proteins (14e85% of VS) and lipids (2e62% of VS) (Bohutskyi et al., 2014). Golueke et al. (1957) introduced the anaerobic digestion of algal biomass Chlorella and Scenedesmus in which the method generated approximately 0.17e0.32 LCH4gVS1. It has been reported that application of microalgae for anaerobic digestion and methane generation were found feasible and cost-effective (Craggs et al., 2011). Some research works reported that species such as: Arthrospora platensis, Chlamydomonas reinhardtii, and Euglena gracilis (Table 3), are used for methane generation by anaerobic digestion and found that methane production capacity is controlled by algal biomass (Raouf et al., 2012). In general, the specific methane production using algal biomass usually ranges between 0.08 and 0.45LCH4g1VS. During anaerobic digestion of Chlorellasorokiniana and Chlorellavulgaris, the transformation of algal biomass into biogas attained up to 72.99% and the increased methane production was attained by dry and milled biomass (Roberts et al., 2016). The high protein fraction of microalgae produces a low C:N

proportion (usually less than10) that can be a serious problem for mono-digestion of algal biomass. In addition, anaerobic digestion of protein mixtures generates ammonia. Excess ammonia level in the reactor may create hindrence to methanogens, resulting the VFA accumulation and reactor failure. To resolve the high VFA accumulation and technical complexity of the anaerobic digestion and biogas generation from algal biomass, still needs further investigation. 5. Mathematical modeling Mathematical modeling helps to minimise the possibility of imbalance and instability in the digestion process at the laboratory scale and in full-scale plants. 5.1. Basic kinetic models Basic kinetic models are based on the rates of bacterial development and wastewater utilisation. Here, the nutrients of the substrate are assumed to be sufficient. The accuracy of models differs with substrate type and digester conditions (Zhen et al., 2015). The first-order kinetic model or the Cone model is used to calculate the hydrolysis rate and the amount of biogas. Using the modified Gompertz model, Zhen et al. (2016) estimated the lagphase duration. The Chen and Hashimoto model presented the greatest difference between the measured and estimated methane yield (6.4e10.7%). Kafle and Chen (2016) used three different kinetic models (the first-order kinetic model, a revised Gompertz model, and the Chen and Hashimoto model) to predict BMP. The Monod equation represents the development rate of microbs as a function of the wastewater levels. The Contois expression is a modified form of the Monod equation. The dual-pooled first-order mode determines the reaction kinetics where the substrates produce a high amount of VAF at the initial stage of the digestion system (Dennehy et al., 2016). Currently, the available kinetic models are based on the constant rate-limiting step. In addition, basic kinetic models are commonly used for laboratory-scale experiments, and may not be feasible to provide an overall scenario for full-scale AcoD implementation. 5.2. Anaerobic digestion model No.1 (ADM1) The International Water Association (IWA) group has developed the most advanced, adaptable and extensible model, ADM1, which is primarily used for wastewater treatment and to forecast and control the AD processes of biogas generation (Batstone et al., 2015). This model was the first to use dynamic processes for forecasting and monitoring biogas generation. The dynamic ADM1 model is a very handy tool to demonestrate and forecast the existing overall phases of an AD system. A well-defined substrate composition and the calibration process run the effective application of modeling. Substrates characterisation and kinetic parameters corresponding to the fragmentation and hydrolysis states can be executed before the numerical simulation. Thus, to set input variables for the ADM1, Girault et al. (2012) suggested a framework of numerical determination for each substrate. Input variables for the ADM1 are selected experimentally, but numerical methods are easily implemented. The kinetic parameters were also predicted and calibrated before the execution of ADM1 simulation (Kil et al., 2017). The initial assumptions of this model are given here: fixed temperature; fixed reactor volume; proper mixing; Perfect microbial states i.e. adequate degradation; Input substrates contains of only Carbon, Hydrogen, and Oxygen; Output of the system contains carbondi

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oxide and methane and no ashes. The basic equation used in the mathematical modeling was reported by Wu et al. (2009). CnHaOb þ (C1)H2O ¼ (C2)CO2þ (C3)CH4

(1)

The difficult phase of the co-digestion system is selecting the proportions of the different parameters. The revised ADM1 model may estimate parameters such as VFA generation, ammonia accumulation, methane production, total and volatile solids, ammonia content, alkalinity (Poggio et al., 2016), and ammonium nitrogen concentration (Demitry et al., 2015) to improve the efficiency of the AcoD process. Co-substrates are heterogenic, and their compositions change dynamically. Thus, a modified ADM1 is required to avoid parameter estimation problems for AcoD system simulation at laboratory and industrial scales. Zaher et al. (2009) employed the powerful “General Integrated Solid Waste Co-Digestion Model” simulation tool to demonstrate reliable simulation of diluted dairy manure and kitchen waste co-digestion. Lately, Flores-Alsina et al. (2016) improved the ADM1 with Fe, P, and S physicochemical and reactions. Near-infrared spectroscopy was applied to estimate the substrate composition, methane production, and kinetics in a modified ADM1, which reduced the analysis time from 30 days to a matter of minutes (Charnier et al., 2017). The ADM1 neglects the thermodynamic features of the process, and the optimisation and stability issues of the degradation process are not yet resolved (Uhlenhut et al., 2018). Although LCFA inhibition has a major impact on the AcoD system (Ren, 2018), to date, this factor has not been accounted for in the ADM1 model for AcoD (Xie et al., 2016). Extensive analysis of substrate characterisation with the ADM1 is needed for industrial-scale use (Nordlander et al., 2017). 5.3. Statistical models Emphasising the relationship between the main parameters (e.g. waste mixing ratio, HRT, C/N ratio, OLR, and temperature) and the outputs (e.g. methane production and VS minimization), statistical models have been developed for the AcoD process (Xie et al., 2012). Two of the most commonly applied statistical methods, central composite design (CCD) and simplex-centroid mixture design (SCMD), are used to optimise the feeding composition of AcoD. The first incorporates several factors, such as waste mixing ratios and C/N ratios, whereas the second takes different proportions of substrate mixtures as variables. CCD utilises response surface methodology (RSM) to optimise the studied parameters. RSM minimises the variability of measured properties and reduces the operation time and costs of production (Montgomery, 2012). By estimating the substrate component and their interactions, SCMD can develop a surface model of continuous variables. Linear, quadratic, full cubic, special cubic, and special quartic models are widely used as the standard forms of the model (Rao and Baral, s-Díaz et al. (2014) applied a four-factor mixture 2011). Page design (a special cubic model with 14 coefficients) to optimise mixture composition and correlate biological systems with statistical outcomes. In co-digestion of FW and poultry manure, a combination of RSM and an artificial neural network (ANN) were used to optimise parameters such as different mixing ratios, pH values, and temperatures (Yusof et al., 2014). Statistical models help to design the initial conditions and parameters of the AcoD system to achieve optimal output.

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CDF modeling process follows the steps shown in Fig. 3. CFD models provide a versatile technique to study flow (Siswantara et al., 2016), turbulence, particle trajectories (Sindall, 2015), the efficiency of the reaction, and gas-to-liquid mass transfer (Leonzio et al., 2016). Bridgeman (2012) used CFD to understand flow patterns, with an emphasis on mechanical mixing. Gas mixing of sludge for AD can be simulated by the novel EulereLagrangian CFD model (Kruis et al., 2015). Several models are used for CFD simulation: the standard ke3 model, the renormalisation-group ke3 model, the realisable ke3 model, the standard keu model, the shear stress transport keu model, and the Reynolds stress model. Using CFD simulation, Wang et al. (2017b) obtained two key parameters, the mean liquid velocity and the mean shear strain rate, to scale up a CSTR-based anaerobic digester. Zhang et al. (2016b) used the CFD model to evaluate the power consumption and mixing mode in anaerobic mono- and co-digestion of corn stover (CS) and cattle manure (CM). They studied the maximum power production for intermittent and continuous mixing was reached with CM/CS ratios of 1:3 and 1:1, respectively. CFD (Sindall, 2015) was used to identify a threshold value of mixing turbulence speed for the substrate in an anaerobic digester for digester stabilisation and gas production. 6. AcoD process for biogas generation in Malaysia Globally, using the AcoD system has become more attractive for the generation of biogas as a renewable energy resource. AcoD technology is commonly used in developed countries, such as Germany (Weiland, 2010), Italy (Bacenetti et al., 2013), Canada (White et al., 2011), the United States (Shen et al., 2015), Denmark (Bojesen et al., 2015), and Norway (Venkatesh and Elmi, 2013). The use of biogas to produce heat, mechanical energy, and electrical energy is not yet common in Malaysia, however. It was reported that utilisation of biogas in this country was considered in the early 1980s. At present, biogas is considered to have great potential for improving sustainable waste management and as a reliable source of energy. Malaysia has a variety of different sources for biogas production as a source of energy, such as palm oil mill effluent (Loh et al., 2017), animal waste (manure, blood, and rumen) (Abdeshahian et al., 2016), SS, and FW (Kumaran et al., 2016). The increasing trend in the generation of various substrates indicates that Malaysia has great potential for producing biogas (Kumaran et al., 2016). Nevertheless, Malaysia remains in the initial stage of implementing the AcoD process for biogas production. In terms of social considerations, the public may protest the establishment of AcoD plants near residential areas (Bong et al., 2017). In addition, FW in Malaysia generally consists of high amounts of fat, which lead to lower biogas production (Bong et al., 2017). Lack of local technology and grid access for biogas power plants hinder the deployment of anaerobic digesters in Malaysia. Most substrate-generating sources are located far from the interconnection points, whereas the maximum distance should be 10 km to minimise power loss from transmission (Chin et al., 2013). Electricity generation from biogas requires high investment. The Malaysian government has launched various financial schemes for the improvement of renewable energy. Shortages of skilled and expert personnel to maintain, operate, and manage the AcoD process slows down the establishment of biogas plants. The government and educational institutions should take part in spreading knowledge and arranging workshops on AD and biogas production to develop such expertise.

5.4. Computational fluid dynamics (CFD) models 7. Future prospects for the AcoD technology Computational fluid dynamics is the use of numerical algorithms for understanding flow patterns within the digesters. The

The increasing trends of the feasibility and applicability of

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Geometry of the Digester

Substrate A + Substrate B

Mixing Turbulence Speed CFD Performance

Boundary States

Zone States

Compute Outcomes

Show Outcomes

Evaluate Outcomes

Fig. 3. Summary of CFD application in AcoD.

biogas production indicate the potential for using different kinds of feedstock waste in the production of biogas through the AcoD process. However, some challenges need to be confronted to convert laboratory-scale production to the industrial level. Many aspects must be maintained, such as buffering capacity, the production rate of biogas, nutritional balance, and microbiological stability. Using co-substrates in the disintegration and hydrolysis steps remains to be investigated. Nanotechnology may be used to monitor and control the process in the form of chips and sensory system. The detailed mechanisms of using heavy metal NPs in the biogas production need further exploration (Abdelsalam et al., 2016). Comparative analysis is required to investigate the impacts of the different seasons on AcoD (Andriamanohiarisoamanana et al., 2016). Further research is required to develop the relationships in between biogas yields from fibre and non-fibre components (Zhang et al., 2016a). The real role of hydrolysis remains to be revealed (Zhen et al., 2016). Efforts are needed to reduce the inhibitory effects of ammonia, VFAs, H2, and sulfides (Zhang et al., 2016a). Bio-char can improve the AcoD process by increasing microbial colonisation surface area, stabilising the buffering, providing sufficient nutrients, and by counteracting substrateinduced inhibition. The effects of continuously fed digestion processes on the interactions between biochar and AcoD microbes and

buffering capacity, and the sorption effect of biochar material, should be investigated (Fagbohungbe et al., 2016). Combinations of different waste materials (Tasnim et al., 2017) and the combined influence of co-digestion may be further confirmed using a continuous digester (Xie et al., 2017). Novel and universal pretreatment strategies are desired. Economic and environmental feasibility analysis should be done in future works for designing favourable pretreatment configurations (Li et al., 2015a). Finally, industrialisation of biogas production will require an appropriate mathematical model. The existing models cannot evaluate the complex properties and the conversion process of the input feedstock efficiently (Batstone et al., 2015). Extensive analysis will make the entire simulation process easier (Uhlenhut et al., 2018). The effects of sulfur, phosphorus, and nitrogen also need to be studied in the future for AcoD models (Xie et al., 2016). Exploration of all these issues is essential to develop a universal model for the AcoD process. 8. Conclusions Biogas generation through AcoD technology from different biodegradable organic substrates is considered a suitable alternative to fossil fuel use. This paper describes the recent research

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development on the AcoD process. The AcoD process is continuously improving with advanced technology to minimise the upcoming challenges. This technology is an economically and environmentally feasible method for biogas generation technique at both the laboratory scale and the industrial scale. The main challenges in biogas production using AcoD technology are evaluating limiting factors and steps, parameter calibration and characterisation, the dynamic behaviour of microorganisms, and characterising the organic materials. Numerous studies have been done to alleviate the problems encountered in biogas production through AcoD technology. However, the process stability and optimisation still require further examination. Integration of the kinetic and thermodynamic modeling, as well as adding inorganic and biological additives, may improve the biogas optimisation process. With advancement of the technology, the AcoD process should continue to improve biogas production. Acknowledgements The authors are thankful to the Faculty of Engineering Technology at the University of Malaysia, Pahang for permitting continuous access to their lab facilities. The present study was made possible by the RDU-160306 grant. References Abdelsalam, E., Samer, M., Attia, Y., Abdel-Hadi, M., Hassan, H., Badr, Y., 2016. Comparison of nanoparticles effects on biogas and methane production from anaerobic digestion of cattle dung slurry. Renew. Energy 87, 592e598. Abdelsalam, E., Samer, M., Attia, Y., Abdel-Hadi, M., Hassan, H., Badr, Y., 2017a. Effects of Co and Ni nanoparticles on biogas and methane production from anaerobic digestion of slurry. Energy Conv. Manag. 141, 108e119. Abdelsalam, E., Samer, M., Attia, Y.A., Abdel-Hadi, M.A., Hassan, H.E., Badr, Y., 2017b. Effects of Co and Ni nanoparticles on biogas and methane production from anaerobic digestion of slurry. Energy Conv. Manag. 141, 108e119. Abdeshahian, P., Lim, J.S., Ho, W.S., Hashim, H., Lee, C.T., 2016. Potential of biogas production from farm animal waste in Malaysia. Renew. Sustain. Energy Rev. 60, 714e723. Abudi, Z.N., Hu, Z., Sun, N., Xiao, B., Rajaa, N., Liu, C., Guo, D., 2016. Batch anaerobic co-digestion of OFMSW (organic fraction of municipal solid waste), TWAS (thickened waste activated sludge) and RS (rice straw): influence of TWAS and RS pretreatment and mixing ratio. Energy 107, 131e140. Agyeman, F.O., Tao, W., 2014. Anaerobic co-digestion of food waste and dairy manure: effects of food waste particle size and organic loading rate. J. Environ. Manag. 133, 268e274. Anbalagan, A., Schwede, S., Lindberg, C.-F., Nehrenheim, E., 2016. Influence of hydraulic retention time on indigenous microalgae and activated sludge process. Water Res. 91, 277e284. Andriamanohiarisoamanana, F.J., Yamashiro, T., Ihara, I., Iwasaki, M., Nishida, T., Umetsu, K., 2016. Farm-scale thermophilic co-digestion of dairy manure with a biodiesel byproduct in cold regions. Energy Conv. Manag. 128, 273e280. Anggarini, S., Hidayat, N., Sunyoto, N.M.S., Wulandari, P.S., 2015. Optimization of hydraulic retention time (HRT) and inoculums addition in wastewater treatment using anaerobic digestion system. Agric. Agric. Sci. Prog. 3, 95e101. Aramrueang, N., Rapport, J., Zhang, R., 2016. Effects of hydraulic retention time and organic loading rate on performance and stability of anaerobic digestion of Spirulina platensis. Biosyst. Eng. 147, 174e182.  lez, R.I., V., Corona-Gonza Arreola-Vargas, J., Flores-Larios, A., Gonz alez-Alvarez, ndez-Acosta, H.O., 2016. Single and two-stage anaerobic digestion for Me hydrogen and methane production from acid and enzymatic hydrolysates of Agave tequilana bagasse. Int. J. Hydrogen Energy 41, 897e904. Bacenetti, J., Negri, M., Fiala, M., Gonz alez-García, S., 2013. Anaerobic digestion of different feedstocks: impact on energetic and environmental balances of biogas process. Sci. Total Environ. 463, 541e551. Batstone, D.J., Puyol, D., Flores-Alsina, X., Rodríguez, J., 2015. Mathematical modeling of anaerobic digestion processes: applications and future needs. Rev. Environ. Sci. Biotechnol. 14, 595e613. Bohutskyi, P., Michael, J., Betenbaugh, M.J., Edward, J., Bouwer, E.J., 2014. The effects of alternative pretreatment strategies on anaerobic digestion and methane production from different algal strains. Bioresour. Technol. 155, 366e372. Bojesen, M., Boerboom, L., Skov-Petersen, H., 2015. Towards a sustainable capacity expansion of the Danish biogas sector. Land Use Pol. 42, 264e277. Bong, C.P.C., Ho, W.S., Hashim, H., Lim, J.S., Ho, C.S., Tan, W.S.P., Lee, C.T., 2017. Review on the renewable energy and solid waste management policies towards biogas development in Malaysia. Renew. Sustain. Energy Rev. 70, 988e998. Bridgeman, J., 2012. Computational fluid dynamics modeling of sewage sludge mixing in an anaerobic digester. Adv. Eng. Softw. 44, 54e62.

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