Techno-Economic Analysis of a Cogeneration System for Post-Harvest Loss Reduction: a Case Study in Sub-Saharan Rural Community

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Article Techno-Economic Analysis of a Cogeneration System for Post-Harvest Loss Reduction: A Case Study in Sub-Saharan Rural Community

Rasaq O Lamidi 1, Long Jiang 1,*, Yaodong Wang 1, Pankaj B Pathare 2 , Marcelo Calispa Aguilar 1, Ruiqi Wang 1, Nuri Mohamed Eshoul 3 and Anthony Paul Roskilly 1

1 Sir Joseph Swan Centre for Energy Research, Newcastle University, Newcastle NE1 7RU, UK; [email protected] (R.O.L.); [email protected] (Y.W.); [email protected] (M.C.A.); [email protected] (R.W.); [email protected] (A.P.R.) 2 Department of Soils, Water and Agricultural Engineering, College of Agricultural & Marine Sciences, Sultan Qaboos University, Muscat 123, Oman; [email protected] 3 The Higher Institute of Industrial Technology Engila, Tripoli 00218, Libya; [email protected] * Correspondence: [email protected]

Received: 8 February 2019; Accepted: 1 March 2019; Published: 6 March 2019

Abstract: Over 90% of global yam production is from West Africa where it provides food and income for above 300 million smallholders’ farmers. However, the major challenge of yam is 10–40% post-harvest losses due to the lack of appropriate storage facilities. This paper assesses a biogas-driven cogeneration system, which could supply electricity and cold storage for ‘yam bank’ within a rural community. Considering 200 households’ Nigerian village as a case study, crop residues are used as anaerobic digestion feedstock to produce biogas, which is subsequently used to power an internal combustion engine. Result shows that the system could store 3.6 tonnes of yam tubers each year and provide enough electricity for domestic and commercial activities. At the current electricity tariff of USD0.013·kWh−1 for rural areas, the system is unable to payback during its life span. The proposed USD0.42·kWh−1 by Nigerian Rural Electriﬁcation Agency seems good with less than 3 years discounted payback period but brings about extra burden on poor rural households. Based on the income from cold storage, electricity tariff of USD0.105·kWh−1 with an interest rate of 4% is suggested to be reasonable which results in 6.84 years discounted payback period especially considering non-monetary beneﬁts of renewable energy system.

Keywords: combined cooling and power; biogas; postharvest loss; cold storage

1. Introduction West Africa accounts for over 90% of global yam production, which yields about 68.132 million tonnes each year as indicated in Table1. It is the major staple food for over 300 million people and provides source of income for many smallholders’ farmers. Yam is second to cereal as the most important food in West Africa. However, a major challenge of yam production in the region is lack of proper storage facility. This usually results in 10–45% post-harvest loss (PHL) and “market glut” during harvesting periods as shown in Figure1[ 1]. Apart from threatening global food security, this wastage also reduces the market share of smallholders’ farmers and thereby put them in continuous poverty circle.

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40 Table 1. Major yam producing countries in 2017 [2]. Table 1. MajorWorld yam (million producing tonnes) countries 68.132 in 2017 [2]. World (MillionNigeria Tonnes) 45 68.132 Ghana 7.119 Nigeria 45 GhanaCote d’Ivoire 5.809 7.119 Cote d’IvoireBenin 3.221 5.809 BeninEthiopia 1.449 3.221 EthiopiaTogo 0.786 1.449 TogoCameroon 0.579 0.786 CentralCameroon African Republic 0.479 0.579 Central African Republic 0.479 Haiti 0.477 Haiti 0.477 ChadChad 0.444 0.444 41

43 FigureFigure 1. 1.A A typicaltypical Nigerian yam yam market market [3]. [3 ].

44 It isIt worth is worth noting noting that that bulk bulk of global of global energy energy poorly poorly resides resides in remote in remote areas areas of Sub-Saharan of Sub-Saharan African 45 regionAfrican (SSA) region [4] where(SSA) [4] access where to access modern to modern energy energy is still is challenging still challenging [5,6]. [5,6]. Grid Grid connections connections to mostto 46 of thesemost settingsof these are settings either are uneconomical either uneconomical or topographically or topographically impossible. impossible. Thus, it Thus, is quite it is desirable quite 47 desirable to deploy micro-grid renewable energy technologies for distributed energy systems in most to deploy micro-grid renewable energy technologies for distributed energy systems in most of these 48 of these villages [7]. These countries mostly lack financial capability to support the required feed-in- villages [7]. These countries mostly lack financial capability to support the required feed-in-tariffs 49 tariffs (FITs) policy for the massive deployments of renewable energy systems. An alternative (FITs) policy for the massive deployments of renewable energy systems. An alternative approach may 50 approach may be synchronisation of electricity delivery with basic agricultural produce processing be synchronisation of electricity delivery with basic agricultural produce processing where income 51 where income from such processing is able to be used to offset the burden of FITs [8,9]. Village or 52 fromfarm such based processing energy demand is able to would be used be tosatisfied offset thewith burden biomass of powered FITs [8,9 ].technologies. Village or farm This basedis because energy 53 demandenergy would demand be and satisfied biomass with resources biomass are powered already technologies.available which This can is be because thermally energy or biologically demand and 54 biomassconverted resources through are gasification already available or anaerobic which digestion can be (AD). thermally It offers or a biologically benefit by supplying converted energy through 55 gasificationand organic or anaerobic fertiliser especially digestion when (AD). used It offers as fuel a benefit for combined by supplying heat and energy power and (CHP) organic systems fertiliser 56 especially[10,11]. whenThis paper used therefore as fuel for accesses combined CHP heat in context and power of cold (CHP) storage systems of yam [10 produce,11]. This and paper renewable therefore 57 accessesenergy CHP tariff inof contextNigeria government. of cold storage Hence, of yamyam producestorage and and renewable renewable energy energy regulation tariff of will Nigeria be 58 government.separately illustrated Hence, yam in storagethe following and renewable subsections. energy regulation will be separately illustrated in the following subsections. 59 1.1. Yam Storage 1.1. Yam Storage 60 Yam storage symbolises “stored wealth” for the farmers, which can be sold anytime of the year. 61 It Yamis stored storage relatively symbolises longer “storedthan other wealth” tropical for crops. the farmers,Traditional which storage can involves be sold leaving anytime tubers ofthe 62 year.unharvested It is stored until relatively when needed. longer than However, other the tropical main crops. issues Traditionalof these techniques storage involvesare sprouting, leaving 63 tubersrespiration unharvested and transpiration, until when which needed. lead However, to both qualitative the main issuesand quantitative of these techniques loss of yam are tubers. sprouting, In 64 respirationaddition toand these transpiration, physiological which related lead problems, to both external qualitative attacks and such quantitative as mould loss growth, of yam insects, tubers. 65 nematodes and mammals infestations are also frequent challenges. Hence, different modern storage In addition to these physiological related problems, external attacks such as mould growth, insects, nematodesEnergies 2019 and, 12 mammals, x; doi: FOR infestations PEER REVIEW are also frequent challenges. www.mdpi.com/journal/energies Hence, different modern storage methods have been investigated [12,13]. Among them, the best solution is to combine fumigation and Energies 2019, 12, 872 3 of 19 storage between 12 ◦C and 15 ◦C at a 70% relative humidity. Storage under these conditions reduces PHL to less than 3% as well as retains the nutritional qualities of yam even after 9 months [14].

1.2. Renewable Energy Regulation in Nigeria Nigerian Electricity Regulatory Commission formulates regulations of fossil and renewable energy systems. The commission fixes and controls tariffs for generation, transmission and distribution companies [15]. For tariff payment, consumers are subdivided into industrials, commercial, residential and government agency. Residential consumers are further divided into four categories i.e., rural, sub-urban, urban and elite-urban consumers. The commission adopts what it called “burden sharing”. Affluent urban elites are required to pay more while rural dwellers only pay USD0.013·kWh−1. Special FITs are also paid in terms of renewable generated electricity. For instance, biomass generated electricity is purchased by the commission at USD0.123·kWh−1 [15]. However, systems below 1 MW are not captured in FITs systems whereas mini, micro and standalone systems often less than 100 kW are quite suitable for sparsely populated rural areas. Consequently, there is no incentive for private sector investment on renewable energy projects in rural area since implementations of FITs for big projects in urban areas also remain ineffective. Currently less than 30% of rural area is connected to national grid while the connected consumers hardly have above 30 h of electricity per week. Comparably, self-generated diesel or gasoline powered generator is very common across the country. The cost of these self-generated electricity varies from USD0.45·kWh−1 to USD0.75·kWh−1 with such generation currently estimated between 8–14 GW. In 2016, commission through Nigerian Rural Electrification Agency (NREA) changed to what it called “demand-driven approach” based on consumers’ willingness to pay. In this approach, internal rate of return (IRR) is pegged at 15% to evaluate consumers’ tariff between USD0.24·kWh−1 and USD0.75kWh−1 depending on the plant size. Then the government will pay no FITs. However, some of these projects are assisted with some grants by the government. Hence, a rural community would sign a binding purchasing agreement with the private investors while government ensures that the interests of both parties are protected. Based on the above backgrounds, this paper aims at comprehensively appraising technical and economic viability of a distributed combined cooling and power (CCP) system in context of a Nigerian agrarian community which could be regarded as a representative case in post-harvest storage of yam tubers. Since less research studies are reported on cogeneration for food storage, the results are quite insightful to explore the potentials of this technology in the similar rural areas. With this type of systems, it is revealed that both food and energy security can be simultaneously achieved since locally available biomass is utilized for energy generation, while part of the generated energy is used for crops storage. The produced digestate from the AD system is also useful as organic fertiliser. By adopting such approach both the environmental impact of the energy generation and agriculture are reduced. The framework of this study is as follows: Modelling of mass, energy and economic analysis are established in Section2. Then results of heat balance, cooling load and economic evaluation are presented in Section3 followed by conclusions in Section4.

2. Modelling and Methodology Materials used in this study involves secondary data from Nigerian National Bureau of Statistics, Nigerian Federal Ministry of Agriculture and relevant empirical studies. Quantiﬁcations of crop residues from rice, sorghum and soya beans are presented in Table2. Farm sizes of smallholder’s farmers vary between 0.5–5 ha, and an average size of 2.5 ha per household is assumed for this simulation [16,17]. Besides, a common practice within the region is inter-planting of legumes with grains [18]. However, rice is not usually inter-planted while sorghum or maize is traditionally mix-cropped with soybeans. One hectare of the farmer’s land is assumed to be used for rice production while the remaining 1.5 hectare is mix-cropped with sorghums and soybeans. Then the cold storage Energies 2019, 12, 872 4 of 19 unit is designed, which is composed of wood pine, corkboard and concrete as inner layer, insulator and outer layer.

Table 2. The amount of crop residue per rural household.

Crop Residue Rice Sorghum Soybean Yield (kg·ha−1) 2175.2 1239.8 944 Planted/household (ha·yr−1) 1 1.5 1.5 Household production (kg·yr−1) 2175.2 1859.7 1410 Residue type Straw Husk Straw Husk Straw pod Moisture content (%) 12.71 2.37 15 15 15 15 Residue grain ratio (%) 1.757 0.20 1.25 0.20 2.5 1.0 Residue availability (%) 83.5 100 83.5 100 70 100 Residue/household (kg·yr−1) 3191.25 435.04 1941.06 371.94 2467.5 1410

The main work in this section is: (1) evaluation of the biogas generation potentials of cogenerated rice-sorghum-soybeans residues; (2) simulation of power and heat recovery performance (3) modelling of ammonia-water absorption chiller. The above results are then used as the inputs for economic analysis.

2.1. Case Study Area The case study area is Agboko village in Benue State of north-central part of Nigeria, which is located on 7.0316◦ N and 8.403◦ E longitude and latitude. It is categorised as the settlement of less than 1000 households [19]. The inhabitants are predominantly farmers and the major crops are rice, sorghum, soya beans, yam and cassava. Currently four 10 HP diesel powered generators are used for agricultural processing such as cassava grinding and rice shredding. The village’s electricity demands are presented in Table3.

Table 3. Electricity demands of the selected village.

Type Unit Energy Demand (kWh·d−1) Currently Used Households 200 3.5·households−1 None Commercial 4 300 4 × 10 HP generator Health centre 1 180 10 HP × 1 Primary school 1 Unknown None

To quantify the available crop residue, the related ratio has been widely used in the literature [20] which is also adopted in this study as expressed in Equation (1) [21].

Sprod = PRprod × SGR × Savl (1) where Sprod is residue production; PR is households’ crop production, SGR is residue grain ratio; Savl is percentage of the residue for energy recovery. To evaluate the value of PR, crop yield per hectare is obtained from Food and Agricultural Organisation’s database (FAOSTAT). As a result, a 26.90 kg·day−1 per farmer of residue is estimated to 5380 kg·day−1 for 200 households.

2.2. System Design Design of the cogeneration system is indicated in Figure2. Crop residues are ﬁrst crushed and mixed with water. The feed is then conditioned to digestion temperature of 35 ◦C and supplied into digester. The produced biogas passes through ammonia scrubber where CO2 is removed and the resultant biogas is enriched. The biogas is subsequently used to power an internal combustion engine Energies 2019, 12, 872 5 of 19

(ICE). Heat from cooling jacket of the engine is recovered to partially maintain AD process. The heat from the exhaust is also recovered to drive three more absorption chillers with the rated cooling power of 17 kW. Chilled water is used to maintain the temperature of cold storage. Figure3 shows the store for yam storage, which is measured as 6 m × 5 m × 2 m with 1 m peak for the rooﬁng. It is made up of wood pine, corkboard and concrete as inner layer, insulator and outer layer. The thickness is adopted asEnergies 12 mm, 2019 70.5, 12,mm x FOR and PEER 101.6 REVIEW mm for pine, corkwood and brick, respectively. Cold 5 of air 19 is blown across Energies 2019, 12, x FOR PEER REVIEW 5 of 19 the stored yam, which carries away heat of respiration and heat absorbed from the surroundings 146 blown across the stored yam, which carries away heat of respiration and heat absorbed from the 146 to maintainblown across internal the stored temperature yam, which at 15 carries◦C. The away average heat volume of respiration of yam and tuber heat is absorbed 1.83 × 10 from−3 m the3 [13 ]. 147 surroundings to maintain internal temperature at 15 °C. The average volume of yam tuber is 1.83×10−3 147 Weightsurroundings of yam tuber to maintain varies internal between temperature 2.5 kg and at 5.5 15 ° kgC. The [12], average and 3.0 volume kg is taken of yam into tuber account is 1.83×10 in−3 this 148 m3 [13]. Weight of yam tuber varies between 2.5 kg and 5.5 kg [12], and 3.0 kg is taken into account 148 study.m3 [13]. The Weight designed of structureyam tuber can varies store between about 3.62.5 tonneskg and of5.5 yamkg [12], tubers. and 3.0 kg is taken into account 149 in this study. The designed structure can store about 3.6 tonnes of yam tubers. 149 in this study. The designed structure can store about 3.6 tonnes of yam tubers.

150 150 151 Figure 2. Schematic of the designed system for cooling and power cogeneration. 151 FigureFigure 2. 2.Schematic Schematic of of the the designed designed systemsystem for cooling and and power power cogeneration. cogeneration.

(a) (b) (c) (a) (b) (c) 152 Figure 3. Yam storage store: (a) overall shape; (b) inner arrangement; (c) structural cross-section. 152 FigureFigure 3. Yam3. Yam storage storage store: store: (a ()a overall) overall shape; shape; ((bb)) innerinner arrangement; (c (c) )structural structural cross-section. cross-section. 153 2.3.2.3. Anaerobic Anaerobic Digester digester 153 2.3. Anaerobic digester

154 2.3.1.2.3.1. System System Modelling modelling 154 2.3.1. System modelling 155 AvailableAvailable daily daily crop crop residue residue is 5380is 5380 kg kg with with the the average average moisture moisture content content and and total total solids solids of 14.23% of 155 Available daily crop residue is 5380 kg with the average moisture content and total solids of 156 14.23% and 85.77%, respectively. Crop residue to water ratio of 1: 1.5 is adopted to obtain a 10% total 156 and14.23% 85.77%, and respectively. 85.77%, respectively. Crop residue Crop to residue water to ratio water of ratio 1:1.5 of is 1: adopted 1.5 is adopted to obtain to obtain a 10% a total 10% solidtotal in 157 solid in the digestion system since AD process performs well with total solid less than 15% [22]. 157 thesolid digestion in the system digestion since system AD process since AD performs process wellperforms with well total with solid total less solid than less 15% than [22 ]. 15% Therefore, [22]. 158 Therefore, daily feedstock loading rate is 45730− 1L·day−1 while 15 and 28 days hydraulic retention time 158 dailyTherefore, feedstock daily loading feedstock rate loading is 45,730 rate L is·day 45730 L·daywhile−1 15while and 15 28and days 28 days hydraulic hydraulic retention retention time time are 159 are considered for thermophilic and mesophilic processes. The required reactor volume is 1143.23 m3 159 consideredare considered for thermophilic for thermophilic and mesophilic and mesophilic processes. processes. The requiredThe required reactor reactor volume volume is 1143.2 is 1143.2 m whilem3 160 while actual volume is 1257.63 m3 with additional 10% remained for gas holding and missing volume. 160 actualwhile volume actual isvolume 1257.6 is m 1257.6with m additional3 with additional 10% remained 10% remained for gas for holding gas holding and and missing missing volume. volume. Then 161 Then AD process is simulated under mesophilic◦ (35 °C) and thermophilic◦ (55 °C) conditions to 161 ADThen process AD is process simulated is simulated under mesophilic under mesophilic (35 C) and (35 thermophilic °C) and thermophilic (55 C) conditions (55 °C) conditions to determine to 162 determine the most suitable operation for the proposed system. 162 thedetermine most suitable the most operation suitable for operation the proposed for the system. proposed system. 163 Heat balance for AD system is accessed based on the following requirements: (1) Heat required 163 HeatHeat balance balance for for AD AD system system is is accessed accessed based based on on the the following following requirements: requirements: (1)(1) HeatHeat required to 164 to warm substrate from atmospheric temperature of 25 °C to operating temperature of 35 °C/55 °C; 164 warmto warm substrate substrate from from atmospheric atmospheric temperature temperature of 25 of ◦25C to°C operatingto operating temperature temperature of of 35 35◦C/55 °C/55◦ °C;C; (2) 165 (2) Heat loss by radiation, convection and conduction; (3) Reaction heat from biochemical activities 165 (2) Heat loss by radiation, convection and conduction; (3) Reaction heat from biochemical activities 166 Heatof reactor’s loss by radiation,microorganisms. convection and conduction; (3) Reaction heat from biochemical activities of 166 of reactor’s microorganisms. 167 reactor’sThe microorganisms. required heat for substrate warming up could be expressed as equation 2. 167 The required heat for substrate warming up could be expressed as equation 2. 푄 = (푀 × 퐶 × ∆푇)/3600 (2) 푄 = (푀 × 퐶 × ∆푇)/3600 (2) 168 where Ms is mass of substrate; ∆T is temperature difference between ambient and digestion 168 where Ms is mass of substrate; ∆T is temperature difference between ambient and digestion 169 temperature; Cp is specific heat capacity. 169 temperature; Cp is specific heat capacity. 170 Heat loss by radiation is evaluated according to equation 3. 170 Heat loss by radiation is evaluated according to equation 3.

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The required heat for substrate warming up could be expressed as Equation (2).

Qwm = (Ms × Cp × ∆T)/3600 (2) where Ms is mass of substrate; ∆T is temperature difference between ambient and digestion temperature; Cp is speciﬁc heat capacity. Heat loss by radiation is evaluated according to Equation (3).

A Q = ε × σ × T4 − T4 × d (3) R d amb 1000 where ε is emissivity of the outer brick wall (0.92); σ is Stefan-Boltzmann constant (5.670367 × 10−8 −3 −4 2 kg·S ·K ); Ad is area of the digester (m ); Td and Tamb are digestion and ambient temperatures (K), respectively. AD encompasses some natural biochemical reactions. Some are endothermic while others are exothermic. Disintegration of protein is usually endothermic while fragmentation of lipid and carbohydrate molecules tend to be exothermic [23]. These are represented with change of enthalpy as illustrated in Equations (4)–(6). From these equations, biochemical heat can be evaluated from proximate composition of feedstock.

0 −1 C6H12O6 → 3CO2 + 3CH4 ∆HR = −138.5 kJ·mol (4) 0 −1 C3H7NO2 + 2H2O → 3CO2 + 3CH4 + 2NH3 ∆HR = +198.5 kJ·mol (5) 0 −1 C16H12O6 + 14H2O → 9CO2 + 22 CH4 ∆HR = +544.5 kJ·mol (6) The digester is insulated with 0.2 meter polyurethane foam with a thermal conductivity of 0.026 W·m−1·K−1. Air gap between materials makes convection heat loss negligible while the conductive heat loss could be evaluated as Equation (7) [24].

(TD − Tair) Qins = (7) 1 Sins × 1000 A kins where Qins is heat lost through insulator; Td is digestion temperature; Sins is insulator’s thickness; kins is thermal conductivity of insulator and A is area of the digester. Total heat required for the digestion system is evaluated as Equation (8).

Qtotal = Qwm + QR + Qins (8)

2.3.2. Process Simulation Thermodynamic performance can be accessed by using an AP process simulator. Systems are broken down into unit operations, which are represented by AP blocks and inputs/outputs of the blocks. Different unit operations are connected with streams. Operating conditions must be supplied i.e., ﬂow rates, compositions, temperature, pressure and appropriate ﬂuid package [8]. Composition of the feed used for the simulation is shown in Table4.

Table 4. Features of crop residues used for the study [25].

Moisture Crude Volatile Crude Fibre Ether Crop Ash (%) Content (WB) Protein (%) Solids (%) (%) Extracts (%) Rice 12.71 5 80 40 3 20 Sorghum 15 4 96 35 3 5 Soybeans 15 12 95 46 7 5 Energies 2019, 12, 872 7 of 19

AD system is often governed by four complex processes i.e., hydrolysis, acidogenesis, acetogenesis and methanogenesis which work together to produce methane and CO2 as illustrated in Figure4. Modelling of AD is based on International Water Association (IWA) AD model 1 [26]. Kinetic reactions are adapted and compositions are adjusted for its suitability to the present work. The aforementioned processes are divided into two reaction sets. Reaction set 1 represents hydrolysis stage which is symbolised with the stoichiometric reactor. The fractional conversion is ﬁxed and it represents the degree of degradation of major biomass components: carbohydrates, protein and lipids. Reaction set 2 is composed of the last three stages of the above processes and modelled with rigorous continuous stir tank reactor (RCSTR). Non-Random Two-Liquid model (NRTL) is selected as the property method due to its suitability to compare and estimate the mole fractions and activity coefﬁcients of individual Energies 2019, 12, x FOR PEER REVIEW 7 of 19 compounds, while also enables liquid and gas phase in the biogas production. The AD process is 202 validatedcoefficients as reported of individual by our compounds, previous work while [8]. also enables liquid and gas phase in the biogas 203 production. The AD process is validated as reported by our previous work [8].

204 205 FigureFigure 4.4. Schematic diagram diagram of of anaerobic anaerobic digestion digestion process. process.

206 2.4.2.4. Simulation Simulation of of Combined combined cooling Cooling and and power Power unit. Unit 207 CCPCCP is is composed composed of of an an ICE ICE and and two two heat heat recovery recovery sections. sections. About About 117 117 kW kW heat heat is is recovered recovered from ◦ 208 thefrom cooling the cooling water jacket water ofjacket ICE withof ICE temperature with temperature up to 70up C,to 70 which °C, which is subsequently is subsequently used toused maintain to 209 ADmaintain process. AD The process. second heat The exchanger second heat recovers exchanger 52.8 recovers kW heat from52.8 kW engines heat exhaust. from engines Exhaust exhaust. heat exits 210 atExhaust 120 ◦C, heat which exits is enoughat 120 °C, to which avoid is precipitation. enough to avoid The precipitation. recoveredheat The recovered from exhaust heat isfrom split exhaust into three 211 parts:is split 17.5 into kW three each parts: of which 17.5 iskW used each to of drive which the is desorbers used to drive i.e., DESORBthe desorbers 1, 2and i.e. DESORB 3 of the 17.5 1, 2kW and Robur 3 212 absorptionof the 17.5 chillerkW Robur as shownabsorption in Figure chiller5 as. Thus, shown three in Figure chillers 5. Thus, could three produce chillers about could 26.22produce kW about cooling 213 power,26.22 which kW cooling equals power, 206.71 which MWh equals·yr−1. The206.71 prime MWh·yr mover−1. The modelled prime mover is a 72 kWmodelled internal is a combustion 72 kW 214 engineinternal (Caterpillar combustion Inc., engine UK). The (Caterpillar engine is Inc., modelled UK). The with: engine (1) a compressor is modelled where with: (1) combustion a compressor air flow 215 rate,where isentropic combustion efficiency air flow and rate, compression isentropic ratio efficiency are the and inputs; compression (2) a stoichiometric ratio are the reactor inputs; with (2) a fuel 216 stoichiometric reactor with fuel flow rates, pressure and combustion reaction as specified; (3) an flow rates, pressure and combustion reaction as specified; (3) an expander with isentropic efficiency 217 expander with isentropic efficiency and discharge pressure defined. Details of prime mover and and discharge pressure defined. Details of prime mover and absorption chiller are presented in Table5. 218 absorption chiller are presented in Table 5. Moreover, detailed validation of the ICE could refer to Moreover, detailed validation of the ICE could refer to our previous work [9]. 219 our previous work [9].

220 Table 5. Parameters of the internal combustion engine (ICE) and the absorption chiller.

Items Parameters Amount Power (kW) 72 Fuel consumption (Nm3·h−1) 42.2 Ambient air temperature (°C) 25 Jacket water temperature (°C) 99 10.5:1 Compression ratio Internal combustion engine Combustion air flow rate (m3·h−1) 292 Displacement (L) 10.5 Exhaust stack temperature (°C) 581 Exhaust gas flow rate (m3·h−1) 324 Heat rejection to jacket water (kW) 99 Heat rejection to lubricant oil (kW) 16 Power (kW) 17.5 Absorption chiller Nominal water flow rate (m3·h−1) 2.77

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Temperature change (ΔT) (°C) 5.5 Water capacity pressure loss (kPa) 29 Ambient operating temperature 0–45 (°C) Thermal input (kW) 25 Electric power (kW) 0.84

221 Figure 6 indicates AP simulation of the 17.5 kW Rabur ammonia-water absorption chiller 222 (AWAC). The detailed modelling and validation could refer to the reference [27]. AWAC consists of 223 absorber, desorber, condenser, evaporator, rectifier and a pump. A refrigerant heat exchanger and a 224 pre-absorber are used to enhance internal heat recovery. The cycle starts from stream 1 with the feed 225 (component, flow rate, mass concentration), efficiency and discharge pressure specified for the pump. 226 The stream exiting pump 2 is first used to cool refrigerant 7. This process knocks out more water 227 molecules from the refrigerant and increases its purity. Heat rejected in the process serves as heat 228 duty of the rectifier. Weak solution 3 and strong solution 11 first meet in PREAB where both pre- 229 absorption and heat exchanging occur. The rejected heat is used to heat stream 5 before reaching the 230 desorber. The air-cooled absorber is modelled as counter-current heat exchanger. Weak stream 231 exiting absorber (1B) is expected to be liquid. Hence, zero vapour fraction, air flow rates and 232 temperature are the designed parameters for the absorber. Similarly, condenser is modelled as heat 233 exchanger with vapour fraction, cooling air temperature and flow rates as the inputs. Evaporator is 234 also simulated as heat exchanger. The designed parameters are vapour fraction for stream 10, while 235 hot streams’ inlet and outlet temperatures are specified as 7 °C and 12 °C, respectively. Desorber is Energies 2019, 12, 872 8 of 19 236 modelled with the reactive column due to its suitability for absorption, stripping, extractive 237 distillation and ordinary distillation.

238 239 Figure 5. 5. AspenAspen Plus Plus (AP) (AP) model model of ofinternal internal combustion combustion engine engine (ICE) (ICE) and andheat heatrecovery. recovery.

Table 5. Parameters of the internal combustion engine (ICE) and the absorption chiller.

Items Parameters Amount Power (kW) 72 Fuel consumption (Nm3·h−1) 42.2 Ambient air temperature (◦C) 25 Jacket water temperature (◦C) 99 Compression ratio 10.5:1 Internal combustion 3· −1 engine Combustion air flow rate (m h ) 292 Displacement (L) 10.5 Exhaust stack temperature (◦C) 581 Exhaust gas flow rate (m3·h−1) 324 Heat rejection to jacket water (kW) 99 Heat rejection to lubricant oil (kW) 16 Power (kW) 17.5 − Energies 2019, 12, x; doi: FOR PEER REVIEW Nominal water flow rate (m www.mdpi.com/journal/energies3·h 1) 2.77 Temperature change (∆T) (◦C) 5.5 Absorption chiller Water capacity pressure loss (kPa) 29 Ambient operating temperature (◦C) 0–45 Thermal input (kW) 25 Electric power (kW) 0.84

Figure6 indicates AP simulation of the 17.5 kW Rabur ammonia-water absorption chiller (AWAC). The detailed modelling and validation could refer to the reference [27]. AWAC consists of absorber, desorber, condenser, evaporator, rectifier and a pump. A refrigerant heat exchanger and a pre-absorber are used to enhance internal heat recovery. The cycle starts from stream 1 with the feed (component, flow rate, mass concentration), efficiency and discharge pressure specified for the pump. The stream exiting pump 2 is first used to cool refrigerant 7. This process knocks out more water molecules from the refrigerant and increases its purity. Heat rejected in the process serves as heat duty of the rectifier. Weak solution 3 and strong solution 11 first meet in PREAB where both pre-absorption and heat exchanging occur. The rejected heat is used to heat stream 5 before reaching the desorber. The air-cooled absorber is modelled as counter-current heat exchanger. Weak stream exiting absorber (1B) is expected to be liquid. Hence, zero vapour fraction, air flow rates and temperature are the designed parameters for the absorber. Similarly, condenser is modelled as heat exchanger with vapour fraction, cooling air temperature and flow rates as the inputs. Evaporator is also simulated as heat exchanger. The designed parameters are vapour fraction for stream 10, while hot streams’ inlet and outlet temperatures are specified as 7 ◦C and 12 ◦C, respectively. Desorber is modelled with the reactive column due to its suitability for absorption, stripping, extractive distillation and ordinary distillation. Energies 2019, 12, x FOR PEER REVIEW 9 of 19 Energies 2019, 12, 872 9 of 19

240 Figure 6. AP model of Rabur Absorption chiller. 241 Figure 6. AP model of Rabur Absorption chiller. 2.4.1. Evaluation of Cooling Load

242 2.4.1. EvaluationThe required of cooling cooling load load Qc,stor could be expressed as Equation (9).

243 The required cooling load Qc,stor could be expressed as equation 9. Qc,stor = Qs + Ql + Qres (9) 푄, = 푄 + 푄 + 푄 (9) where Q symbolises sensible cooling load, which is the heat required to cool yam tubers from ambient 244 where Qs symbolisess sensible cooling load, which is the heat required to cool yam tubers from 245 ambienttemperature temperature to storage to storage temperature; temperature;Ql denotes Ql denotes heat loss heat from loss cold from room; coldQ room;res is the Qres respiratory is the heat 246 respiratorygenerated heat by generated yam tubers. by Hence,yam tubers. the sensible Hence, the cooling sensible load cooling is evaluated load is asevaluated Equation as (10).equation 247 10. Qs = MwCp,w(Ta − Ts) + MyCp,y(Ta − Ts) (10) 푄 = 푀퐶,(푇 − 푇) + 푀퐶,(푇 − 푇) (10)

248 wherewhere MwM andw and My Marey theare mass the mass of moisture of moisture and dry and matter dry matter in the in tuber; the tuber; Cp,w andCp,w Cp,yand are Cspecificp,y are heat speciﬁc heat 249 capacitiescapacities of water of water and and yam; yam; Ta andTa Tands areT ambients are ambient and storage and storage temperature. temperature. Moisture Moisture content contentof yam of yam − ◦ − 250 tubertuber is 65% is 65% (wet (wet basis) basis) while while its specific its speciﬁc heat heatcapacity capacity is 2.152 is kJ·kg 2.152-1 kJ·oC·kg−1 [28].1· C 1 [28]. 251 At At the the storage storage temperature, temperature, yam yam is is dormant dormant and its its sprouting sprouting is is avoided avoided but but respiration respiration continues −1 −−11 −1 252 continuesbecause because the yam the tuber yam is tuber a living is a tissue. living Respiratorytissue. Respiratory rate of rate yam of cells yam is cells 3 mL is CO 3 ml2·kg CO2·kg·h ·h. According. 253 Accordingto the reference to the reference [29], relationship [29], relationship between between CO2 produced CO2 produced during during respiration respiration and and heat heat released is 254 releasedcalculated is calculated as Equation as equation (11). 11. Q = M × 1.08485× 10−2 (11) 푄 = 푀h × 1CO.084852 × 10 (11)

255 wherewhere Mco2M isco2 theis themass mass of CO of2 COreleased.2 released. 256 RespiratoryRespiratory heat generation heat generation yam is calculated yam is calculated as equation as 12. Equation (12). 푄 = (12) . Qh Qres = (12) 257 Heat losses are conduction, convection and radiation.3.6 Cold storage is insulated with of 258 corkboard,Heat losses which are is conduction, placed between convection inner pinewood and radiation. and outer Cold concrete storage blocks. is insulated Air gap with between of corkboard, 259 thesewhich materials is placed is almost between unavoidable. inner pinewood Heat loss and by convection outer concrete is regarded blocks. to Air be gapnegligible. between Total these area materials 260 in iscontact almost with unavoidable. air is 109 m Heat2. Thermal loss by conductivities convection isof regarded pinewood, to corkboard be negligible. and Totalconcrete area are in 0.151 contact with 261 W·mair−1 is·K 109−1, 0.0433 m2. ThermalW·m−1·K−1 conductivitiesand 0.762 W·m−1 of·K pinewood,−1, respectively. corkboard Therefore, and heat concrete loss per are square 0.151 meter W·m −1·K−1, 262 through0.0433 the W ·wallm−1 of·K −cold1 and storage 0.762 can W· bem− defined1·K−1, respectively.as equation 13 Therefore, [30]. heat loss per square meter through the wall of cold storage can be deﬁned as Equation (13) [30]. 푄 = × (13) Ts − Ta 1 Ql = × (13) Energies 2019, 12, x; doi: FOR PEER REVIEW R T 1000 www.mdpi.com/journal/energies Energies 2019, 12, 872 10 of 19

where RT is total heat resistance of materials, which could refer to Equation (14). 1 Sp Scb Sc RT = + + (14) A Kp Kcb Kc where Sp, Scb and Sc represent thickness of pinewood; corkboard and concrete; Kp, Kcb and Kc are their thermal conductivities. Heat loss by radiation is calculated according to Equation (15).

A Q = ε × σ × T4 − T4 × (15) R s a 1000 where Ts denotes absolute temperature of hot body; Ta is absolute temperature of cold surroundings; A represents the area of cold store in contact with cold air.

2.4.2. Evaluation of the Cogeneration System

Electrical efﬁciency of CCP system µelc is deﬁned as Equation (16).

W µelc = × 100% (16) Mbiogas × LHVbiogas where W is the output electricity; Mbiogas and LHVbiogas signify mass ﬂowrate and low heat value of biogas. The efﬁciency of CCP system µCCP-storage is evaluated as Equation (17).

W + (Qevap × µstorage ) 3µCCP−storage = × 100% (17) Mbiogas × LHVbiogas where Qevap is the evaporator duty of the absorption chiller and µstorage is thermal efﬁciency of cold storage unit. The efﬁciency of digestion system µdigestion could be evaluated as Equation (18).

Qtotal − Qlost µdigestion = × 100% (18) Qtotal where Qtotal is total heat supplied to digestion system while Qlost is the heat loss from digestion system through insulation and radiation. The overall efﬁciency of the system µover could be calculated as Equation (19).

W + Qtotal + QGen µover = (19) Mdigestion × LHVdigester

2.5. Economic Evaluation Costs of the proposed system are obtained from Nigeria’s National Electricity Regulation Commission [15]. Table6 indicates the inputs used for economic analysis. Cost of cold storage is obtained from local manufacturers while that of the chilling unit is taken from online suppliers. Income from cold storage is evaluated using differences in the prices of yam during harvesting and off-season as obtained from the local market. The scenarios are considered as follows: (1) Electricity is sold at the above prices; (2) Yam is sold locally or exported. These are evaluated considering 7%, 9% and 20% interest rates, which are typical of lending rates from Nigerian bank of agriculture, bank of industry and commercial banks respectively. Lifespan of 20 years and 85% availability are assumed. Besides neither salvage value nor inﬂation rates is considered in the study. About 10% of the electricity generated is used onsite. Net present value (NPV), discounted payback period (DPP), and levelised Energies 2019, 12, 872 11 of 19 cost of energy (LCOE) are adopted to assess the economic feasibility of the system. Cold storage is driven by the recovered waste heat, therefore additional incomes from sales of yam is treated as income from sales of heat with regard to LCOE calculation.

Table 6. Inputs for economic analysis.

Parameters Amount Capital cost (AD + ICE system) (USD·kW−1) 2900 Capital cost (cold storage)a (USD·unit−1) 2000 Capital cost (chiller)b (USD·unit−1) 35,508.18 Fixed O&M (AD + ICE system) (USD·kW−1·yr−1) 53.5 Variable O&M (AD + ICE) (USD·MWh−1) 0.95 Variable O&M (cold storage) (USD·MWh−1) 0.15 Fuel cost (USD·MWh−1) 5 Parasitic load (%) 10 Life Span (Yr) 20 Interest rates (%) 7, 9, 20 Capacity (kW) 72 Availability (%) 90 Exchange rate (USD·#−1) 305 Price of yam tuber (fresh) (USD·tuber−1) 0.82 Price of yam tuber (off-season) (USD·tuber−1) 1.64 Price of yam tuber (export) (USD·tuber−1) 3.25 Electricity price (rural grid) (USD·kWh−1) 0.013 Electricity price (Self-generated) (USD·kWh−1) 0.75 Electricity price (REA) (USD·kWh−1) 0.42 FITs Biomass (N·MWh−1) 37,357 Replacement (60000h) (USD·kW−1) 1389.77 Total project cost 357,324.50 a Nigerian manufacturers, b Online suppliers.

NPV is calculated as Equation (20).

N Fn NPV = ∑ n (20) n=0 (1 + d) where Fn is net cash ﬂow in year; n is analysis period; d is annual interest rate. LCOE could be expressed as Equation (21).

N Qn LCOE = TLCC + ∑ n (21) n=0 (1 + d) where TLCC is total life-cycle cost; Qn is lifespan energy savings or produced; d is annual interest rate while n is project lifespan. Discounted cash inﬂow (DCI) could be evaluated as Equation (22).

Real cash inflow DCI = (22) (1 + d)n DPP could be evaluated as Equation (23).

BDCI DPP = ADCI + (23) CDCI where A is the last period with negative cumulative DCI; B is the absolute value of cumulative DCI at the end of period A; C represents DCI during the period after period A. Energies 2019, 12, 872 12 of 19

3. Results and Discussions Simulation results under both mesophilic and thermophilic conditions are presented in Table7. Expectedly, under thermophilic condition it produces 14.6% more biogas (293.56 L·kgVS−1·day−1) than the system operated under mesophilic condition (256.16 L·kgVS−1·day−1). However, the percentage methane of mesophilic system is 64.8% while that of thermophilic is 58.2%. Thus speciﬁc methane production from the thermophilic process is only 3.26% higher than that from the mesophilic process. Considering the requirements for biogas cleaning and additional heat required for the thermophilic system, the mesophilic process is recommended. From the simulation results of the gas engine, about 511 MWh of electricity can be generated per annum by the system. However, only 459.9 MWh will be sold since 51.1 MWh is self-utilized. Moreover, the system is able to store 3.6 tons of yam tubers per year. This is enough to provide electricity for 200 households and for the commercial agricultural processes presented in Table3.

Table 7. Comparison of thermophilic and mesophilic processing conditions.

Items Mesophilic Process Thermophilic Process Operating temperature (◦C) 35 55 Percentage methane (%) 64.80 58.20 Speciﬁc biogas production (L·kgVS−1·day−1) 256.16 293.56 Speciﬁc methane production (g·kgVS−1·day−1) 190.26 196.80

3.1. Heat Balance of AD System Heat balance related to microbial activities is presented in Table8. That heat is inputted (+) or generated (−) indicates endothermic and exothermic processes, respectively. It is worth noting that the entire process is exothermically producing heat of about 16.26 kW. However, this biochemical enthalpy change remains unchanged under mesophilic and thermophilic processing conditions. This is because it is influenced by feedstock’s proximate composition and flowrate, which are kept constant in terms of both scenarios. Total heat requirements for processing conditions are indicated in Table9. Results showed that total heat required by the mesophilic AD system is 47.38 kW which includes heat for the substrate warming up, biochemical heat of reaction, heat loss through insulation and heat loss by radiation. Heat required for the substrate warming up accounts for over 90% of the required heat. However, the impact of heat loss to the surroundings becomes significant as the digestion temperature increases. For instance, using thermophilic digestion temperature (55 ◦C) as against mesophilic 35 ◦C would have increased heat loss to the environment by 57.14%.

Table 8. Biochemical energy balance of anaerobic digestion (AD) system.

Percentage Molar Mass Daily Flow ∆H0 Enthalpy Heat Composition R (Dry Basis) (g·mole−1) (kg·day−1) (kJ·mole−1) (kW) Carbohydrates 64.44 180 3466.87 −138.50 −30.87 Protein 7.00 89 376.6 +198.50 +9.72 Lipids 4.33 300 232.95 +544.50 +4.89 Total heat of enthalpy −16.26

Table 9. Total heat load of AD process.

Required Load (kW) Mesophilic (35 ◦C) Thermophilic (55 ◦C) Substrate warming up +62.72 +188.16 Biochemical heat of reaction −16.26 −16.26 Heat loss through insulation +0.88 +2.64 Heat loss by radiation +0.0386 +0.312 Total heat load required 47.38 174.85 Energies 2019, 12, 872 13 of 19

3.2. Cooling Load As indicated in Table 10, the required cooling load is 35.50 kW. The highest cooling load requirement is the sensible cooling, which is required to cool the tubers from atmospheric temperature to the 15 ◦C storage temperature. To maintain this storage temperature, heat generation through respiration is carried away while the system’s heat loss should be compensated accordingly. The design of the system must compensate this cooling load. Exhaust heat is only able to power three 17.5 kW ammonium-water absorption chillers. From the AP simulation, each of the three chillers produced about 8.77 kW cooling power, which is not able to meet the total cooling load. Thus another 25 kW single stage ammonium-water absorption chillers is adopted to produce 9.27 kW cooling load, which is consider to be driven from hot water of ICE. Energies 2019, 12, x FOR PEER REVIEW 13 of 19 Table 10. Cooling load required. 343 Table 10. Cooling load required. Particulars Required Load (kW) Particulars Required load (kW) SensibleSensible cooling cooling load required load required 34.83 34.83 Respiratory heat generated 7.43 × 10−2 Respiratory heat generated 7.43×10−2 Heat loss through insulation 5.93 × 10−1 Heat loss through insulation 5.93×10−1 Heat loss by radiation 1.43 × 10−3 −3 Total coolingHeat loadloss requiredby radiation 1.43×10 35.50 Total cooling load required 35.50 3.3. Efficiency of the System 344 3.3. Efficiency of the system 345 ElectricalElectrical efficiency efficiency ofof thethe proposed system system is is26.73%. 26.73%. This This efficiency efficiency is below is below the range the of range 28– of 346 28–39%,39%, which which is is reported reported for for many many spark spark ignition ignition ICE. ICE. The The reason reason may may be beattributed attributed to its to low its low air-fuel air-fuel 347 ratioratio of of 6.85 6.85 when when compared compared to to the the valuevalue aroundaround 14.1 for for many many standard standard engines. engines. Nevertheless, Nevertheless, the the 348 engineengine is specificallyis specifically designed designed to to workwork withwith relatively impure impure low-grade low-grade fuels fuels and and it is it expected is expected that that 349 somesome of of the the features features of of the the high-grade high-grade fossilfossil fuel driven driven engines engines might might have have been been compromised compromised for for 350 thethe design design driven driven by by biogas. biogas. ElectricalElectrical efficiency efficiency is is shown shown in in Figure Figure 77 in in terms terms of of various various ambient ambient 351 temperatures.temperatures. Unlike Unlike other other gas turbines, gas turbines, the increase the increase of atmospheric of atmospheric temperature temperature does not does significantly not 352 affectsignificantly the efficiency. affect Onethe efficiency. remarkable One fact remarkable is that the fact efficiency is that the of efficiency the proposed of the systemproposed is significantlysystem is 353 influencedsignificantly by ambientinfluenced temperature. by ambient Whentemperature. temperature When temperature increases from increases 20 ◦C from to 50 20◦C, °C the to efficiency50 °C, 354 couldthe efficiency be reduced could by 47.2%.be reduced The by performance 47.2%. The performance becomes even becomes worse even when worse temperate when temperate is higher is than 355 50 higher◦C. However, than 50 the°C. However, average temperature the average oftemperature the study areaof the is study around area 27.5 is around◦C with 27.5 22 ◦°CC with in the 22 coldest °C 356 monthsin the ofcoldest December months to of January December and to 33January◦C for and the 33 hottest °C for monthsthe hottest of months February of February to April. to Hence, April. the 357 Hence, the efficiency is not expected to be significantly affected by the variations in the atmospheric efficiency is not expected to be significantly affected by the variations in the atmospheric temperature. 358 temperature.

30 28 26 24 22 20 18

Electrical Electrical efficeiency (%) 16 14 20 25 30 35 40 45 50 o 359 Ambient temperature ( C)

360 FigureFigure 7. 7.Electrical Electrical efﬁciencyefficiency vs. various various ambient ambient temperatures. temperatures.

361 Besides,Besides, methane methane compositions compositions of of the the biogas biogas vary vary from from 0% 0% to 80%to 80% as shownas shown in Tablein Table 11. 11. Its effectsIts 362 oneffects electricity on electricity output and output exhaust and exhaust temperature temperature is demonstrated is demonstrated in Figure in Figure8. It is 8. indicated It is indicated that energythat 363 energy content of fuel greatly determines the power production of the engine. This is because heat 364 content of fuel defines the amount of its available chemical energy. In order to meet the electric power 365 and heat demand, it will be uneconomical to operate the system with fuel less than 60% methane 366 purity, which justifies extra costs on biogas scrubbing.

367 Table 11. Composition of biogas fuel.

S/N Gas Methane CO2 1 Base 0.705 0.295 2 Bio80 0.800 0.200 3 Bio70 0.700 0.300

Energies 2019, 12, x; doi: FOR PEER REVIEW www.mdpi.com/journal/energies Energies 2019, 12, 872 14 of 19

content of fuel greatly determines the power production of the engine. This is because heat content of fuelEnergies deﬁnes 2019 the, 12, amount x FOR PEER of REVIEW its available chemical energy. In order to meet the electric 14 of 19power and heat demand, it will be uneconomical to operate4 Bio60 the system0.600 with fuel0.400 less than 60% methane purity, which justiﬁes extra costs on biogas scrubbing.5 Bio50 0.500 0.500 6 Bio40 0.400 0.600 Table 11. Composition of biogas fuel. 368 Co-efficiency of performance (COP) for absorption cooling system is calculated as 0.51 for the S/N Gas Methane CO2 369 exhaust driven system while absorption unit driven by hot water has a COP of 0.40. Thus, a total 35.5 370 kW cooling load is produced1 from 76 BasekW heat supplied 0.705 to the generators. 0.295 The calculated energy 2 Bio80 0.800 0.200 371 utilisation efficiency of the digestion system is 98%. According to the composition of the feedstock, 3 Bio70 0.700 0.300 372 the digestion process 4 is exothermic which Bio60 means that0.600 the internal heat 0.400 generated by microbial 373 activities is high enough5 to offset most Bio50 of the heat that 0.500is being lost to the 0.500 environment. Thus, the 374 efficiency of the system6 is 54.89% when Bio40 heat is only recovered 0.400 for cooling. 0.600 Comparably, the overall 375 system efficiency is 72.45% when heat is recovered for cooling and AD process. 100 800 Power 80 Exhaust temp. C)

600 o

60 400 40 Power (kW) Power

200

20 ( temperature Exhaust

0 0 376 Base Bio67.5Bio80 Bio70 Bio60 Bio50 Bio40 377 FigureFigure 8. 8.Effect Effect of of biogasbiogas compositioncomposition on on power power output. output.

378 3.4.Co-efficiency Economic evaluation of performance results (COP) for absorption cooling system is calculated as 0.51 for the exhaust driven system while absorption unit driven by hot water has a COP of 0.40. Thus, a total 379 Effects of various electricity prices and interest rates on NPV are presented in Figure 9. It is 35.5 kW cooling load is produced from 76 kW heat supplied to the generators. The calculated energy 380 observed that profitability of the system is sensitive to both electricity price and interest rate. At utilisation efficiency of the digestion system is 98%. According to the composition of the feedstock, the 381 current electricity price of USD0.013·kWh−1 for rural consumers, NPV remains negative regardless of 382 digestionincome process from local is exothermic or foreign sales which of yam means tubers. that When the internal electricity heat is generated sold at USD0.105·kWh by microbial−1 activities, i.e. 25% is 383 highof enoughproposed to selling offset mostprice of USD0.42·kWh the heat that is−1, beingNPV is lost positive to the in environment. most cases but Thus, subject the to efficiency interest rate. of the 384 systemThe calculation is 54.89% when results heat show is onlythat recoveredit becomes foruneconomical cooling. Comparably, when interest the rate overall is above system 10.5%. efficiency Table is 385 72.45%12 shows when effects heat isof recoveredinterest rates for on cooling LCOE. and It is ADdemonstrated process. that LCOE for local sale varies between 386 USD0.115·kWh−1 and USD0.276·kWh−1 when interest rate increases from 7% to 20%. Comparably, 387 3.4.LCOE Economic for foreign Evaluation sale Resultsis lower than that for local sale which ranges from between USD0.111·kWh−1 388 andEffects USD0.272·kWh of various−1. electricityIn order to pricesmake the and system interest feasible, rates rural on NPV price are of electricity presented cannot in Figure less 9than. It is −1 −1 389 observedUSD0.115·kWh that profitability, which could of the also system explain is sensitivethe reasons to for both negative electricity NPVs price at USD0.013·kWh and interest rate. and At 390 −1 −1 currentUSD0.053·kWh electricity price. Also of USD0.013 worth noting·kWh that−1 for NPV rural are consumers, very attractive NPV remains at USD0.42·kWh negative regardless and 391 USD0.21·kWh−1. However, these prices are considered too expensive for rural dwellers but a price of of income from local or foreign sales of yam tubers. When electricity is sold at USD0.105·kWh−1, 392 USD0.105·kWh−1 looks reasonable, which is a threshold value among the selected five prices. As i.e., 25% of proposed selling price of USD0.42·kWh−1, NPV is positive in most cases but subject 393 aforementioned, it will become negative when interest rate is higher than 10.5%. Thus the additional to interest rate. The calculation results show that it becomes uneconomical when interest rate is 394 income from sales of yam could be a solution to compensate for the difference between LCOE and above 10.5%. Table 12 shows effects of interest rates on LCOE. It is demonstrated that LCOE for 395 proposed selling price of USD0.105·kWh−1, which becomes achievable if the yam are exported. local sale varies between USD0.115·kWh−1 and USD0.276·kWh−1 when interest rate increases from 7% to 20%. Comparably, LCOE for foreign sale is lower than that for local sale which ranges from between USD0.111·kWh−1 and USD0.272·kWh−1. In order to make the system feasible, rural price of electricity cannot less than USD0.115·kWh−1, which could also explain the reasons for negative NPVs at USD0.013·kWh−1 and USD0.053·kWh−1. Also worth noting that NPV are very attractive

Energies 2019, 12, x; doi: FOR PEER REVIEW www.mdpi.com/journal/energies Energies 2019, 12, 872 15 of 19 at USD0.42·kWh−1 and USD0.21·kWh−1. However, these prices are considered too expensive for rural dwellers but a price of USD0.105·kWh−1 looks reasonable, which is a threshold value among the selected ﬁve prices. As aforementioned, it will become negative when interest rate is higher than 10.5%. Thus the additional income from sales of yam could be a solution to compensate for the difference between LCOE and proposed selling price of USD0.105·kWh−1, which becomes achievable if the yam are exported.Energies 2019, 12, x FOR PEER REVIEW 15 of 19

2000 Local yam/$0.013 kWh-1 Local yam/$0.053 kWh-1 1500 Local yam/$0.105 kWh-1 Local yam/$0.21 kWh-1 -1 1000 Local yam/$0.42 kWh Foreign yam/$0.013 kWh-1 Foreign yam/$0.053 kWh-1 500 -1

NPV (USD) NPV Foreign yam/$0.105 kWh Foreign yam/$0.21 kWh-1 -1 0 Foreign yam/$0.42 kWh

-500 Interests 7% Interests 9% Interests 20% 396 Figure 9. Net present value (NPV) vs. various interest rates and sale prices. 397 Figure 9. Net present value (NPV) vs. various interest rates and sale prices. Table 12. Effects of interest rates on levelised cost of energy (LCOE). 398 Table 12. Effects of interest rates on levelised cost of energy (LCOE). Interest Rate (%) Local (USD·kWh−1) Foreign (USD·kWh−1) Interest rate (%) Local (USD·kWh−1) Foreign (USD·kWh−1) 7 0.115 0.111 97 0.124 0.115 0.111 0.120 209 0.2760.124 0.120 0.272 20 0.276 0.272

399 ProfitabilityProfitability index index indicates indicates a similar a similar trend trend with with NPV, NPV, which which is shown is shown in in Figure Figure 10 10.. Profitability Profitability 400index cannotindex becannot negative. be negative. When the When value the is much value larger is much than larger zero, than the project zero, the becomes project more becomes profitable. more −1 401Thus, theprofitable. highest profitThus, couldthe highest be obtained profit atcould USD0.42 be obtained·kWh atwhen USD0.42·kWh interest rate−1 when is 7%. interest Payback rate periods is 7%. 402in terms Payback of various periods electricity in terms prices of various and interestelectricity rates prices are and shown interest in Table rates 13 are. Atshown the electricityin Table 13. selling At the − 403price ofelectricity USD0.013 selling·kWh price1, the of project USD0.013·kWh is unable−1 to, the payback project duringis unable the to plant’s payback life during span. Athe discounted plant’s life 404payback span. period A discounted of 11.54 years payback is obtained period at of USD0.105 11.54 years·kWh is −obtained1, which at could USD0.105·kWh be considered−1, which to be could feasible be 405for ruralconsidered energy project to be feasible especially for whenrural placedenergy inproject context especially of other when non-monetary placed in context revenues. of other non- 406 monetary revenues. Table 13. Payback5 periods at various electricity prices and interest rates.

Interest Rates USD0.013·kWh−1 USD0.105·kWh−1 USD0.420·kWh−1 4 Interest rate 7% 7% negative Interest rate 9% 11.5 2.01 9%3 negative Interest rate 20% 18.6 2.48 20% negative negative 4.70 2

1 Profitability index 0

-1

$0.013·kWh-1 $0.105·kWh-1 $0.42·kWh-1 407 Electricity price 408 Figure 10. Profitability index vs. various electricity prices and interest rates.

Energies 2019, 12, x; doi: FOR PEER REVIEW www.mdpi.com/journal/energies Energies 2019, 12, x FOR PEER REVIEW 15 of 19

2000 Local yam/$0.013 kWh-1 Local yam/$0.053 kWh-1 1500 Local yam/$0.105 kWh-1 Local yam/$0.21 kWh-1 -1 1000 Local yam/$0.42 kWh Foreign yam/$0.013 kWh-1 Foreign yam/$0.053 kWh-1 500 -1

NPV (USD) NPV Foreign yam/$0.105 kWh Foreign yam/$0.21 kWh-1 -1 0 Foreign yam/$0.42 kWh

-500 Interests 7% Interests 9% Interests 20% 396

397 Figure 9. Net present value (NPV) vs. various interest rates and sale prices.

398 Table 12. Effects of interest rates on levelised cost of energy (LCOE).

Interest rate (%) Local (USD·kWh−1) Foreign (USD·kWh−1) 7 0.115 0.111 9 0.124 0.120 20 0.276 0.272

399 Profitability index indicates a similar trend with NPV, which is shown in Figure 10. Profitability 400 index cannot be negative. When the value is much larger than zero, the project becomes more 401 profitable. Thus, the highest profit could be obtained at USD0.42·kWh−1 when interest rate is 7%. 402 Payback periods in terms of various electricity prices and interest rates are shown in Table 13. At the 403 electricity selling price of USD0.013·kWh−1, the project is unable to payback during the plant’s life 404 span. A discounted payback period of 11.54 years is obtained at USD0.105·kWh−1, which could be Energies 2019, 12, 872 16 of 19 405 considered to be feasible for rural energy project especially when placed in context of other non- 406 monetary revenues. 5

4 Interest rate 7% Interest rate 9% 3 Interest rate 20%

1 Profitability index 0

-1

$0.013·kWh-1 $0.105·kWh-1 $0.42·kWh-1 407 Electricity price 408 FigureFigure 10. 10.Proﬁtability Profitability index index vs. vs. variousvarious electricity prices prices and and interest interest rates. rates.

The rural electriﬁcation agency’s price of USD0.420·kWh−1 looks good with DPP of 2 years. However, it puts extra burden of higher electricity tariff payment on impoverished rural dwellers majority of whom are currently living below poverty line of USD2·day−1. However, with the income from the cold storage of agricultural products and 7% interest rate, the project has a DPP of 11.54 years Energies 2019, 12, x; doi: FOR PEER REVIEW www.mdpi.com/journal/energies and 9.3 years for local and foreign sales of yam respectively when electricity is sold at USD0.105·kWh−1. Therefore, a reduced interest rate around 4% is welcome for rural electriﬁcation investors. At this interest rate, DPP is around 6.84 years, which is comparable to the payback periods of renewable energy systems across the world.

4. Conclusions A bio-gas driven CCP system is evaluated to post-harvest storage of yam tubers within a rural community. Both mass and energy balance are presented, and the results are further used for economic analysis. Conclusions are yielded as follows: It is worth noting that a sustainable farming is achievable in rural areas. Agricultural residues can be successfully used to generate decentralised distributed power. Heat recovered for cold storage of agricultural produce and AD system are capable of increasing the system’s efficiency from 26.73% for the electricity generation only to about 72.45% for CCP system. This efficiency is a function of the extent of purification of the biogas, which determines quantity and quality of the recoverable heat. Also internal heat generation by AD system plays major role in offsetting the effect of heat loss to the surrounding and it becomes significant when the operational temperature of AD is increased. Heat loss is increased by 57.14% when the digestion is operated at 55 ◦C against mesophilic digestion at 35 ◦C. Therefore, considering the previously mentioned, mesophilic digestion process is recommended. All economic indices are negative at the current rural electricity tariff of USD0.013·kWh−1 while extra income from the combined system is not enough to offset the difference between the cost of electricity generation and rural selling price. The proposed NREA USD 0.42·kWh−1 looks good with less than three years payback periods but puts burden of payment on poor rural households. However, with the income from cold storage and electricity price of USD0.105·kWh−1 (25% of the NREA proposed tariff) an 11.54 years DPP is achievable which can be reduced to 6.84 years if the interest rate could be reduced to 4%. The lower electricity price with a shift special loan scheme is therefore recommended for promoting renewable energy systems in rural areas, which becomes more attractive when electricity delivery is combined with agricultural product processing. Energies 2019, 12, 872 17 of 19

The case study is quite insightful for rural areas in most places of West Africa based on techno-economic analysis in this paper and also the proposed CCP system using AD process could be applied for cold storage of other agriculture produce.

Author Contributions: Conceptualization, R.L. and P.P.; methodology, R.L.; software, R.L.; validation, M.A.; formal analysis, R.L.; investigation, L.J.; resources, R.L.; data curation, L.J.; writing—original draft preparation, R.L. and L.J.; writing—review and editing, R.W.; supervision, L.J.; Y.W.; A.R.; project administration, A.R.; funding acquisition, A.R.; N.E. Funding: This study is partly supported by the Engineering and Physical Science Research Council (EPSRC), UK (RE4Food project -EP/L002531/1); EPSRC IAA Phase 2 (EP/K503885/1)–‘Computational fluid dynamics (CFD) enabled optimisation of a hybrid solar dryer for sub-Saharan Africa; EPSRC Global Challenges Research Fund Institutional Sponsorship Award 2016–Institutional Sponsorship Funding, ‘Preparing for GCRF Award: Optimisation of different solar dryers used in Sub-Saharan Africa using computational fluid dynamics (CFD)’. Acknowledgments: The authors wish to thank the Nigeria’s Petroleum Technology Development Fund for sponsoring this research work. Conflicts of Interest: The authors declare no conflict of interest.

Nomenclature

A Area (m2) AD Anaerobic digestion AP Aspen Plus AWAC Ammonia-water absorption chiller −1 ◦ −1 Cp Specific heat capacity (kJ·kg · C ) CCP Combined cooling and power CHP Combined heat and power DCI Discounted cash inflow DPP Discounted payback period f Cash flow (USD) FITs Feed in tariffs HX Heat exchanger HP Horsepower ha Hectare hr Hour I Investment cost (USD) ICE Internal combustion engine IWA International water association LCOE Levelised cost of energy M Mass (kg) NREA Nigerian rural electrification agency NPV Net present value (USD) NRTL Non-Random two-liquid model n Period (year) PHL Postharvest loss PI Profitability Index Q Heat (kW) r Interest rate (%) RCSTR Rigorous continuous stir tank reactor SSA Sub-Saharan African region T Temperature (◦C) TLCC Total life cycle cost (USD) VS Volatile solids yr Year Energies 2019, 12, 872 18 of 19

Greek letters

µ Efﬁciency (%) ε Emissivity of the outer brick wall ơ Stefan-Boltzmann constant

Subscripts amb Ambient bio Biogas CO2 Carbon dioxide d Digestion e Electricity gen Generator h Heat over Overall res Respiration s Sensible stor storage w Water wm Warm y Yam

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