Techno-Cost-Benefit Analysis of Biogas Production from Industrial Cassava Starch Wastewater in Thailand for Optimal Utilization with Energy Storage

Abstract

This paper applied the optimization model of the biogas utilization pathway with the biogas utilization availability assessment to examine the effect of biogas system parameters on biogas utilization. The model analyzes the biogas utilization pathway availability and maximum profit to value added and productivity in biogas from industry wastewater in Thailand. The results showed that profit and availability of biogas utilization reduce biogas loss to flare, that it entails several conversion processes. The scenario for the biogas utilization pathway and storage with biogas production technology improves. Evaluated are operation time, waste and energy demand to the cassava starch usage during the production for 50–1000 tons per day. Five mature biogas production technologies were benchmarked evaluated based on the chemical oxygen demand removal efficiency and biogas yields. Subsequently, low-, medium-, and high-pressure storages and a battery storage were considered and discussed in this paper as suitable energy storage for each desired biogas plant operation. Five biogas utilization pathways, including converting biogas into thermal energy, generating electricity, and upgrading biogas to compressed biogas, were then compared. Those improved options in the scenario select suitable biogas technologies, storage, and application for value-added, reduce the environmental problems and renewable energy production from wastewater.

1. Introduction

Biogas technology was implemented to replace aeration treatment ponds of factories in Thailand. Initially, most biogas systems were primarily for wastewater treatment rather than alternative energy production. The Energy Policy and Planning Office (EPPO), Ministry of Energy, Thailand, promoted biogas technology by launching the “Biogas Promotion Project for Industry” (BPPI) since 2008–2015. The project provided an investment subsidy for a biogas plant in agro-industry [1]. The Energy Research Institute (ERI), Chulalongkorn University, Thailand, organized the program. The project involved 227 factories from five relevant agro-industries: cassava starch, palm oil refinery, ethanol, food, and rubber. Among those factories registered in the program, 53 cassava starch factories produced biogas from wastewater processing.

Additionally, there were five biogas technologies which were implemented in Thailand, including up-flow anaerobic sludge blanket (UASB), anaerobic covered lagoon (ACL), and continuously stirred tank reactor (CSTR), anaerobic fixed film reactor (AFFR), and hybrid reactor (HBR). Those technologies attribute different efficiencies and investments, which are essential factors. They are also related to wastewater odor, methane emission, and fuel prices. However, some plants wasted biogas by flaring without any benefit. That was due to the oversized or non-optimal design of the biogas system and the storage system. Furthermore, discontinuous raw materials caused unbalanced thermal energy utilization.

In terms of the cassava starch process, relevant literature was reviewed, including Tran et al. [2], who presented the energy, water usage, and carbon footprints of cassava starch production in Thailand, Vietnam, and Colombia. The study introduced and demonstrated an environmental indicator, such as greenhouse gas (GHG) emissions, to evaluate whether the project was feasible and useful [3]. Anyanwu et al. showed that the successful program of cassava yield and biogas production could reduce cassava peel and wastewater [4]. Ghimire et al. studied baseline GHG emissions, and waste management to the cassava starch industry is energy. The result presented that biogas has the highest energy recovery with GHG alleviation potential [5].

In the biogas technology sector, some researchers developed the efficiency of biogas technology, including Lu et al. who studied methane that can be produced through anaerobic digestion using cassava starch wastewater [6]. The critical process parameters in an UASB reactor for wastewater treatment, operated by the days factor, were determined to comprehensively evaluate the performance. Phowan and Danvirutai presented a result, the highest efficiency of chemical oxygen demand COD removal, hydrogen yield, and methane yield compared with one-stage and two-stage CSTR processes [7]. Cavaleiro et al. showed the performance of an anaerobic hybrid sludge bed-fixed film reactor and used a neural network model and the fuzzy logic control performance [8].

Although the biogas production process is relatively continuous and stable, the biogas consumption rate can cause the system to be unbalanced. Therefore, storage is essential for balancing the biogas utilization. It determines the optimal size. In the study of Poul Alberg Stergaard, the effects of heat storage, biogas storage, and electricity storage on the performance of a fully renewable energy scenario [9] were studied. Furthermore, a high-pressure type of room was studied by Guan et al. [10]. Regards to methane sorption on a volumetric rig, with higher pressure, Castello et al. studied the behavior of different carbon materials in methane storage to obtain the best carbon material adsorption capacity by unit volume of adsorbent [11]. Kapdi et al. studied gas storage [12]. The biogas upgrading for the vehicle to extend the potential of biogas production with animal waste. Rios et al. modeled a cylindrical stainless-steel vessel filled with granular activated carbon as a natural gas storage system [13]. The hydrogen sulfides gas (H₂S) removal unit was essential before using biogas. F. Osorio and J.C. Torres studied the removal of H₂S for biogas from the digesters of a pilot plant with chemical gas scrubbing technology [14]. Pipatmanomai et al. conducted a feasibility study on a biogas-to-electricity system with and without H₂S removal by activated carbon, as the H₂S adsorbent [15]. Further, relevant to H₂S removal technologies, includes the work of Waewsak et al. [16].

The optimization method is mainly employed to solve technical problems in the industry [17]. There are numerous optimization tools available in which the non-linear binary program (NLBP) [18] found as a solution to the reliability of an electric distribution system [19]. However, optimization tools explicitly designed for biogas utilization pathways were the cost-benefit of electricity [20]. As an evaluation of biogas plant economics, [21] analyzed the economic performance of electricity production and compared the NPV and PB of the cost. Using GIS data for the investment model, the pricing, and optimization of natural gas storage in the competitive natural gas markets, they also reviewed that the estimated and optimum gas storage process eases the gas market impacts on the model [22], and optimization energy storage arrangement with a wind turbine, the analysis on cost, size, and technology [23]. Dominkoi et al. focused on investment management for a biomass tri-generation system combined with pit thermal energy storage (PTES) [24].

Therefore, this study aims to thoroughly investigate the key factors of operation cassava starch manufacturing, efficiency and cost of the biogas system. The possible options of biogas utilization pathways are explored for enhancing the biogas production efficiency and improving the renewable energy performance of the cassava starch industry. The optimization scheme using NLBP for maximum-profit biogas utilization pathway is the production of seasonal yield, conversion, storage technologies, and together with the biogas utilization availability assessment (BUAA). The study focuses mainly on biogas utilization, reduction in flare loss, and energy conservation. Results from implementation to real-world agriculture industries plants are shown in terms of a techno-cost-benefits as cleaner energy production and firm renewable energy plants.

2. Materials and Methods

2.1. Cassava Starch Factory

The biogas supply chain from cassava starch factories defined the seasonal yield, which was operating 5–12 months from providing the raw material. The plants are classified into three sizes (capacity), as shown in Figure 1. The data analysis from survey 53 cassava starch factories for the model uses different operation time, energy, and waste. The scenario contains vital data with a high impact variable by working day and energy demand. The raw materials impact the number of operations, which was an advantage and a high profit for financial analysis. The medium and large factory has a farmer cooperative system for a continuous supply of raw material.

In contrast, small factories could not buy raw material to follow market prices that cause less workday. The low energy demand provides various utilization of biogas and value-added. Likewise, small factories are less likely to choose a high investment of the biogas system because of less of a working day and high energy demand. Therefore, the critical data were significant for the scenario model.

In the process, 1 ton of cassava starch requires 3.91–5.03 tons of cassava roots (26.9% starch). They were cleaned by washing grime from the rind tapioca peel, including soil and sand. The fresh roots were then chopped into small pieces using a grinding machine before rasping to be a slurry, comprising starch, water, fibrous residue, and impurities. After dewatering in a decanter machine, protein separation, and dehydration took place, and the product was packaged. For the drying process, hot air was used from a hot-oil boiler or steam boiler. The energy demand and wastewater discharge from the process is shown in Figure 1.

2.2. Analysis of Biogas Production

There are five technologies considered in this work: UASB, ACL, CSTR, AFFR, and HBR. Among those production technologies, UASB was used to generate biogas. The UASB reactor comprises a built-in three-phase precipitator for the effective separation of gas, liquids, and solids (sludge). The wastewater was pumped into the UASB tank at the bottom of the reactor and it flowed to the top. It mixed the sludge granules of the microorganisms. Figure 2a shows a diagram of the UASB system.

The ACL is popular in livestock farms in Thailand. This kind of digester requires a large area, which can be in-ground or lined. The lagoon is covered with flexible or floating gas-tight materials. The retention time is usually 30–45 days or longer, depending on design. Figure 2b presents a diagram of a covered lagoon system. The difference of ACL with UASB is that, in ACL, the biogas kept on top of the holder is wastewater. This simple passive digester can produce methane from wastewater with low maintenance and cost, improving efficiency given in Figure 2f.

The CSTR diagram is shown in Figure 2c. The steel digester tank comprises a motor which is an agitator for the mixing of solid waste. The agitator motor operates for a given period. The methane produced is fed directly to a generator or boiler without the biogas holder for some plants. Therefore, for the methane production design, the equipment capacity must properly match the size.

The AFFR process is associated with the attached biological growth, take the filter media in the reactor tank. Ideally, the press will usually provide a thin microorganism film with contact wastewater. The most common type of filter media includes plastic sheet media (vertical and crossflow). Stationary media may also include submerged media, detailed in Figure 2d which shows an AFFR system that is similar to the UASB system, the only difference of the filter media is in the reactor.

The HBR is a combined system of each technology for the highest yield of methane production. Figure 2e shows a sample of a hybrid system matching two techniques between a covered lagoon and CSTR. For this system, the ACL has a high volume of storage, and the CSTR has an agitator for mixing bacteria, which reduces bacteria settlement on the ground of the digester.

Data of the five technologies from 227 biogas plants were analyzed for COD removal efficiency, the ratio of biogas production, and investment, as shown in Figure 2f. The radar plot shows the COD removal efficiency in blue, the production yield in red, and the green investment cost. It is noted that while all the technologies meet the COD removed requirements and give the same amount of production yield, the CSTR is the cost that is far more expensive than the others. However, in the next section, more details in examining the biogas utilization pathway are described.

Five biogas technologies, UASB, ACL, CSTR, AFFR, and HBR, are considered in the paper. Among those production technologies, data of the five biogas technologies from 227 biogas plants were analyzed for COD removal efficiency, the ratio of biogas production, and investment, as shown in Figure 2f. The graph plot shows the COD removal efficiency, the production yield, and the investment cost. It is noted that while all the technologies meet the COD removed requirements and give the same amount of production yield, the CSTR is the cost that is far more expensive than the others. However, in the next section, more details in examining the biogas utilization pathway are described.

2.3. Biogas Production

The investigated biogas production technologies are effects on biogas utilization pathways, five biogas technologies considered in the optimization model for methane production. Equations (1) and (4) calculate the volume of methane production in a unit of normal cubic meters (Nm³) per year and hour. The COD is input with the averaged value of 15,000 mg L⁻¹, as obtained from 53 factories is data:

COD_{loading n} = C_n × D_n × W_n × COD,

(1)

COD_{removal i, n} = COD_{loading n} × Effr_{i, n},

(2)

Cap_{i, n} = COD_{removal i, n} × Me_{i, n},

(3)

Br_i ≤ Cap_{i, n}.

(4)

Biogas System Investment Cost

The investment costs are calculated by the study in calculating the linear cost function. The cost price cover the range of factory sizes in Thailand (50–400 tonnes per day). The profit of the study has a minimum value that has been beneficial for the financial model. The large size model checks and adjusts costs before calculating Equation (5), (9), (12), (14), (16), (18), (21), and (24). These data were the average value from the actual implement of BPPI.

The investment cost (Inv_j) of the biogas systems for the five technologies studied, defined as USD kg⁻¹ COD_removal, is used for Equation (5), and listed in Figure 2f:

Inv_i = COD_{removal i, n} × CB _{i, n}.

(5)

2.4. Storing Biogas and Energy

A gas vessel comprises a covered lagoon direct to stored methane gas. Typically, the scenario classifies three pressure levels: low-, medium-, and high-pressure systems.

2.4.1. Low-Pressure Storage: T₁

A biogas plant mainly uses low-pressure storage, as in covered lagoon technology. A high-density polyethylene (HDPE) is stored in the methane gas. It is the thickness of 0.75–2 mm and stores a high volume, up to approximately 100–100,000 Nm³. The investment cost of low-pressure storage is significantly lower than medium- and high-pressure systems. The cover of a lagoon pond was installed and used for UASB, CSTR, AFFR, and HBR systems.

The design used for the scenario I, which used biogas for the cassava process is thermal energy for three factory sizes and the surplus biogas converted to electricity. A scale of low-pressure biogas storage is given by Equation (6). The capacity storage for thermal energy and electricity sales of the whole day:

T₁ for S₁

T₁₁ = V_k0 (if V_k0 < De),

(6)

T₁ for S₂

T₁₂ = V_k1 = V_k0–De.

(7)

The size of the storage for the boiler and on-peak period is given by Equation (8), for which biogas’ capacity is kept for off-peak hours, from 10 p.m. on Friday to 9 a.m. on Monday, ~59 h of storage:

T₁ for S₃

T₁₃ = 59 × (V_k3/H_o).

(8)

T₁₃ = 59 × (V_k3/H_o).

(8)

The investment cost of the low-pressure system:

CT_1,3 = 6.94 USD × T_L1,2,3.

(9)

2.4.2. Medium-Pressure Storage: T₂

Medium-pressure storage is typically used for small systems, for approximately 100 Nm³/day at 7 bar, with a steel tank. The biogas from the food waste system is used to store, which reduces the plant area, and the gas can be delivered long distances. The investment cost is more than double that of low-pressure systems—the volume of storage is given by Equations (10) and (11):

T₂ for S₂

T₂₂ = V_k2/P_c,

(10)

T₂ for S₃

T₂₃ = (59 × (V_k3/H_o))/P_c.

(11)

The investment cost of medium-pressure storage is calculated as 90.55 USD per m³ (including the compressor unit):

CT_21,2,3 = 90.55 USD × T_M1,2,3.

(12)

2.4.3. High-Pressure Storage Used for Compressed Biogas after Upgrading: T₃

High-pressure storage, typically at 200 bar, is suitable for storing compressed biogas (CBG) used for vehicle fuel. This CBG system has a high methane content, usually of more than 95%, and a tank volume of approximately 10–10,000 Nm³. Regularly, the high-pressure tank removes CO₂ before compressing the methane.

The calculated size of T₃:

T₃ for S₅

T₃ = V_k5/H_o/0.552,

(13)

The investment cost calculate 24.14 USD Nm⁻³

CT₃ = 24.14 USD × T₃.

(14)

2.4.4. Battery Storage: T₄

In addition to storing biogas in pressure vessels to accommodate fluctuations and peaks, biogas can feed directly into electricity and is stored in a battery. In this form, there is no need for gas-tight containers or dome for storage facilities.

Battery systems were usually in renewable energy systems like solar cells and wind turbines. There are many types of variable costs per W-h. Among these, lead-acid battery is a mature technology and generally has a low investment cost. Technically, a large capacity is discharged. However, this may not necessarily be the only option.

For whole-day case—No use:

T₄ for S₄

T₄ = (V_k4 × B_e)/D_n × B_t.

(15)

As per data from suppliers in Thailand, the investment cost of the battery system was 905.52 USD per 1000 Ah:

CT₄ = 905.52 USD × (T₄/1000).

(16)

Equations (6)–(14) determine the sizes of biogas storage and energy storage. The size of biogas storage depends on the scenarios analyzed. As an indicator of pressure tank storage, three levels, low, medium, and high, categorize the pressure. Moreover, as energy storage, a battery was chosen for the scenario of converting biogas to electricity for selling whole day or on-peak periods. In this regard, different capacities of biogas storage and sizes of the battery are considered for the optimal utilization pathways in terms of economics and revenue.

2.5. Gas Preparation and Biogas Utilization Pathway Analyses

H₂S is typically associated with biogas from the digester. It is harmful and strongly caustic to many types of steel. H₂S burns, it forms a sulfur oxide, and acid matter, and rusts iron, such as the biogas engine. Practically, biogas contains 100–500 mg Nm³ of sulfur dioxide. Technically, there are three leading solutions to remove H₂S from biogas: biological, chemical, and physical. The H₂S removal system costs around 286.74 USD Nm³ of biogas. Meanwhile, the price of a gas humidity unit is 63.89 USD Nm⁻³ of biogas. For this study, defining five biogas utilization scenarios of cassava industries are examined as follows.

2.5.1. Thermal Energy for Drying Process: S₁

The preliminary energy was the supply for the drying. The biogas was a replacement for the heavy oil, pricing at approximately 0.452 USD l⁻¹. The thermal energy demand mainly depends on the production of the factory. Cassava factory size categories are recalled in Figure 1.

The size of the burner was calculated, subject to a maximum capacity of cassava starch processing. Here, the consumption of biogas was approximately 50–100 Nm³ ton⁻¹:

BUs = V_k0/H_o.

(17)

A biogas burner replaces the existing oil burner. The investment cost was 301.84 USD Nm³h⁻¹. It includes a pipe system:

Buc = 301.84 × Bus.

(18)

The energy of biogas used for S₁ to calculate the low heating value of biogas that replaced heavy oil is:

E_k0 = V_k0/0.526.

(19)

2.5.2. Electricity Generation via the Gas Engine to the Grid for the Whole Day: S₂

With the given assumption that a cassava starch factory has excess biogas, after on-site use, the surplus biogas was converted to electricity to a grid operator, for a whole day, as 0.0721 USD kWh⁻¹. This scenario allows a biogas plant as a simple operation with a low investment cost and small-size systems.

The calculation of the S₂ scenario includes the generator size and cost and provides equipment and energy value.

The size of biogas engine-alternator set (kW) for selling electricity on the whole day:

GW_size = (V_k1 × B_e)/H_w.

(20)

Investment cost of S₂:

GW_cost = 0.953 USD × GW_size.

(21)

The energy to convert biogas to electricity, likely of scenarios S₂, S₃, and S₄:

E_k(2,3,4) = (V_k2,3,4 × B_e).

(22)

2.5.3. Electricity Generation via the Gas Engine to the Grid during On-Peak Hours: S₃

Medium-to-large starch processing plants typically have large-size systems, which can be the biogas for selling electricity during the peak periods at 0.0884 USD kWh⁻¹. The on-peak hours in Thailand cover 9 a.m.–10 p.m. Monday–Friday. With the capacity, the excess gas production matched the operating time of 13 h per day.

2.5.4. Electricity Generation via Gas Engine and Store in the Battery for On-Peak Period Selling: S₄

The on-peak period requires a large volume of storing gas. An alternative approach was saving electrical energy in the cell for later on-peak. Considering the safety aspect, high-volume biogas stored at low pressure has the potential risk of explosion.

For electricity only in on-peak periods:

$${\mathit{GP}}_{\mathit{size}} = (V_{k5} \times B_e)/H_{\mathit{on}}.$$

(23)

Investment cost of S₄:

GP_cost = 0.953 USD × GP_size.

(24)

This system is investment cost, including the gas engine-alternator set and electricity distribution, which was approximately 0.953 million USD MW⁻¹ for all methods. The electricity on-peak period with the battery was calculation treated as the same as that of the electricity on-peak period scenario.

2.5.5. Upgrading to Bio-Methane for Vehicle Fuel: S₅

In this scenario, the biogas needed CO₂ removal to obtain a calorific heat value suitable for the vehicle, priced at approximately 0.392 USD per kg-CBG. As a result, the gas volume reduces typically by 40%. There are many processes to remove CO₂ with various costs, consumptions, and efficiencies, e.g., absorption in water both physically and chemically, adsorption with pressure or vacuum changes with zeolite or carbon separation, and cryogenic process, among others. The quality of methane was enhanced such that the compactness of biogas with methane was more than 95%. Hence, the investment cost was approximately 6942.35 USD Nm⁻³ of biogas (including shipping and installing of the plant to Thailand):

E_k5 = V_k6/0.552.

(25)

2.6. Mathematical Formulation

The model formulation has established a base on the constructed system.

Objective Function

The maximize annual profit formulated the objective function. The revenue of biogas is minus the equipment system is investment costs and biogas digester per year for 15 years. Figure 3 shows the conceptual boundary optimization model of biogas utilization:

Maximize profit = (P₀ E_k0 – inv₀) + ${{\displaystyle \sum }}_{k=1}^6\left(Pk E_k-invk\right)\mathrm{I}\mathrm{k}$-${{\displaystyle \sum }}_{i=1}^5\left(invi \mathrm{I}\mathrm{i}\right)$.

Subject to

$$\begin{array}{cc}\hfill {\displaystyle {\displaystyle \sum }_{\mathrm{i}=1}^5}\mathrm{Ii} & = 1\hfill \\ \hfill {\displaystyle {\displaystyle \sum }_{\mathrm{k}=1}^6}\mathrm{Ik}& =\{\begin{array}{l}Sk (V_k-De) if V_k>De\\ 0 \mathit{otherwise}\end{array}\hfill \\ \hfill V_k& ={\displaystyle {\displaystyle \sum }_{\mathrm{i}=1}^5}\mathrm{Bri} \mathrm{Ui} \mathrm{Ii}\hfill \\ \hfill V_k& \le \max \left\{ \mathrm{cap} \mathrm{i} \mathrm{Ii}\right\}\hfill \end{array}$$

For k = 0

E_k = S₁ V_i if V_i < De

For k = 1, 2, 3, 4, 5, 6

$$E_k=\{\begin{array}{l}{S}_k (V_k-De) if V_k>De\\ 0 \mathit{otherwise}\end{array}$$

And I_i = binary I = 1,2…,5.

I_k = binary k = 1,2,3,4,5,6.

Binary coding was applied to manage the algorithm in NLBP. It is the ability to dispose of both integer variables quickly and contuously simple. The calculation has three solution parts with constraints. In the first part, selecting one of the five technologies constrain biogas production, as below.

The maximizing volumetric biogas generates from five technologies (U_i). The subscript i denotes each technology:

U₁ + U₂ + U₃ + U₄ + U₅ = 1.

The utilization part deals with the storage and combines the application of the scenarios, as detailed in

Table 1. Utilization biogas with storage model.

No.	Utilization Pathway	Description	Storage	Model
1	k0	Use biogas to the boiler	Low-pressure	T1S1
2	k1	Use biogas to electricity the whole day if Vk > De	Low-pressure	T1S2
3	k2	Use biogas to electricity the whole day if Vk > De	Medium-pressure	T2S2
4	k3	Use biogas to electricity on-peak periods if Vk > De	Low-pressure	T1S3
5	k4	Use biogas to electricity on-peak periods if Vk > De	Medium-pressure	T2S3
6	k5	Use biogas to electricity on-peak periods if Vk > De	Battery	T4S4
7	k6	Use to upgrade CBG if Vk > De	High-pressure	T3S5

.

3. Results

The proposed NLBP model was formulated in Microsoft Excel. To solve the objective function, the cost-benefit ratio corresponds to each scenario. The input variables included working days, ranging from 150 to 300 days per year. They are depicted in Table 1 and Figure 1 and Figure 3.

The results of small factories. The capacity ranging from 50 to 250 tons of production per day is shown in Figure 1 for all five digesters (U₁—U₅). In the first utilization pathway, the biogas produced was supplied to the boiler. The volume of biogas was just enough to cover conventional Fuel for ~90%. There was no biogas left for other pathways. At the minimum, the working day has a maximum profit of 44.78 USD per ton-COD loading. Biogas is produced from the ACL with low-pressure storage. (T₁S₂). It appears that the ACL has low investment and can store biogas in the digester. However, its disadvantages, as low efficiency and the need for a large area for the plant, become a limitation for higher revenue. The high-performance operation of a plant for 180–300 days. The UASB with low-pressure storage gave maximum profits of approximately 52.48 to 55.42 USD per ton-COD loading, respectively, shown in Figure 4.

Figure 5a shows the investment per ton-COD loading of a biogas plant; ACL has low investment costs while CSTR is high. Figure 5b presents the investment cost regarding the application and storage; ACL has low investment cost because it self-stores biogas, which is advantageous. Figure 5c and d show the profit analysis per investment and ton-COD loading of biogas-to-boiler with low- and medium-pressure (k1 and k2, respectively), ACL has the highest value.

For medium-size factories, the left volume of biogas, approximately 15% produced, was available for other pathways. The maximum profit solution is for 150–210 working days. The ACL used in S₁ and S₃ results in about 44.97, 47.77, and 49.77 USD per ton-COD loading, respectively. The UASB can obtain the maximum profit value for the plant working on 240–300 days with low-pressure storage. The resulting biogas is used in S₁ and S₃. The maximum profits are 51.62, 53.62, and 55.21 USD per ton-COD loading, respectively. Although UASB has the disadvantages of high investment and storage added, it is highly efficient, as shown in Figure 6. Figure 7 shows the different investment values per ton-COD of biogas plants; ACL has the lowest investment, and CSTR has the highest. Figure 8a shows the analysis result of the profit of the five biogas technologies on the k3 scenario; CSTR cannot produce the biogas cover thermal demand, while ACL technology has a high value because of its low investment, but uses a large amount of land for installing the system. The new potential option was to generate electricity on peak time with battery energy storage, which reduces the investment cost of the battery before and after by approximately 50%, compared with the low-pressure storage of three biogas technologies (which have no storage in the digester), as shown in Figure 8b–d.

For large-size factories, there is 450–1000 tons of produce per day. After substituting for conventional fuel, there was 1–19% excess biogas for other utilization pathways. It considered the maximum profit solution for the 150–240 working day plants. The ACL used for k3 has the highest profits of approximately 46.49, 49.11, 50.98, and 52.38 USD per ton-COD loading. For the plants operating for 270–300 days per year, the UASB technology provided the maximum profit value with the k3. The maximum profit was in the range of 53.52–55.15 USD per ton-COD loading, as shown in Figure 9. Comparing to similar profit figures, the covered lagoon can be a potential candidate for UASB. Figure 10 shows the different investment values per ton-COD of biogas plant; ACL has the lowest investment, and CSTR has the highest investment. CSTR technology has a profit per investment because of low demand energy. The profit per investment on k3 and k5 pathways that compare profit before and after battery cost reduce by about 50%. It has a high potential for the future benefit of biogas utilization on the k5 pathway, as shown in Figure 11a–d.

4. Discussion

The effects of five different biogas technologies were used to develop the result. The multiplication of the efficiency COD removal and biogas yields define the indicator of the digester as the multiplication of the energy demand. The evaluation proposes the best solution. The result shows that for the small factories requiring thermal energy demand of about 2.30 PJ/ton, the indicator value should be greater than 0.432 and effective energy conservation. For the plants in which the thermal energy demands are about 1.285 PJ/ton, the indicator value higher than 0.342 is the best practice with any biogas production technology.

More than that, the research work presented the biogas model for available implementation to real-world agriculture industries and which was an added benefit of the clean technology project [3]. The calculation of the scenario focuses on the financial benefit, not including the availability—the availability technologies concept for a link to the notion of liberation and enrichment. They are available to user lives without imposing burdens upon users. That has become effortless, creating an availability assessment for the biogas model. The BUAA evaluation is available for all systems with the four sides. The first, “Instants” was considered for energy demand as random. Second, we were assessed on “Safe” in all spatial and temporal contexts. Third, “Everywhere” was all-encompassing, with no exclusion. The last “Easy” mechanics of technology become invisible to users, as shown in Appendix A. The results of BUAA were k3 and k5 pathways of medium-size factories. This was the first and secondary benefit; the battery has a full score of more than low-pressure storage on-peak. The battery storage was an advantage technology as instant technologies for electricity demand, safety for use and operation of the plant, small area for installation, and smooth process for renewable energy source, as presented in Figure 12. Besides, the biogas plant was applied to the local grid or island grid system that was to investigate another kind of storage and integrate the energy demand side. The sustainability renewable energy source is likewise research of Amiryar and Pullen [25].

5. Conclusions

This paper presents the option biogas value-added which was an optimization model. The scenario showed a techno-cost-benefit evaluation of the biogas utilization pathway. First of all, the essential indicator of the cassava starch manufacturers was the energy demand and some operating days. That has an impact on the diversity of biogas utilization. Likewise, the two key indicators of biogas production of digester were benchmarked based on efficiency COD removal and methane generate yields. Secondary, the kind of storage was significant on a renewable energy source and location to constrain. It is a variable of cost and value-added advantage. Finally, the proposed optimization scheme for biogas utilization pathways with BUAA can be applied to evaluate project implementation of biogas power plants fed with any industrial wastewater. Hence, it can achieve energy sustainability and smart power generation systems.