Design and Simulation-Based Performance Evaluation of a Mixed-Mode Solar–Biomass Grain Dryer for Smallholder Maize Processing in Taraba State, Nigeria

benchmark in the simulation reported later in this paper. Ntwali, Schock, Romuli, Kiria Chege, Banadda, Aseru, and Müller (2021) compared an inflatable solar dryer with direct sun drying for maize in Uganda and reported internal temperatures averaging about 7°C above ambient, with both methods reaching below 14% (w.b.) moisture content within two days, and with a measurable reduction in aflatoxin contamination relative to unassisted sun drying. Nwankwo, Ujam, Ushie, and Ugwu (2022) designed and evaluated a passive solar crop dryer at Caritas University, Enugu, using a collector tilt angle and length-to-width ratio consistent with established passive-collector design guidance, an approach adopted for the collector sizing in Section 3 of this paper. Uwa Ujunwa, Nworie, Agbo, Otah, Famuyibo, Brown, and Ugo (2024) — a study involving co- authors from a Nigerian polytechnic (Kenule Beeson Saro-Wiwa Polytechnic, Bori) — reported a low-cost natural convection solar tunnel dryer for tomato, maize, and mango that achieved a 30-50% reduction in drying time relative to open-sun drying, with chamber temperatures ranging from 25°C to 73°C. On the thermal-efficiency side, Ononogbo et al. (2024) reported a hybrid solar-electric vegetable dryer whose drying chamber temperature rose from 35.9°C to 58.1°C under no-load conditions, while Njoku et al. (2025) reported a smart dual-energy crop dryer with a solar collector thermal efficiency of about 80.8% and a 38-45% thermal gain over ambient conditions. Ikwuagwu, Nwosu, Anyaka, and Unachukwu (2024) used CFD-based simulation (rather than physical testing alone) to optimise a passive solar dryer with multiple collectors, illustrating that simulation is an accepted and published methodology for evaluating solar dryer performance ahead of, or alongside, physical fabrication — an approach mirrored in Section 3.3 of this paper. Collector and system efficiencies reported across this literature span a wide range, from about 10-12% for simple natural-convection units (Tesfaye, Kumsa, & Regassa, 2022, in a related solar tunnel ginger- drying study) to above 80% for actively controlled hybrid units, indicating that the 40-50% mid-range efficiency assumed for the present passive/mixed-mode design (Section 3) is a realistic and conservative planning value. 2.3 Economic and Financial Viability of Solar Drying Beyond thermal performance, several recent studies have examined the financial case for solar drying investment, a dimension this paper extends to in Section 4.6. Akowuah, Bart- Plange, and Dzisi (2021b) carried out a full cost-benefit analysis of a 1-tonne capacity mobile solar-biomass hybrid maize dryer in Ghana, reporting a total capital expenditure of about $5,263, an internal rate of return of 38%, a benefit-cost ratio of 1.48, and a payback period of about 2.7 years, and further estimated that scaled deployment could create several hundred direct youth jobs in the study area. Nwakuba, Okafor, and Okorafor (2020) conducted a techno-economic analysis of a hybrid solar-electric dryer and reported annual energy cost savings with a payback period as low as 0.72 years under favourable utilisation assumptions. Jimoh, Ojo, Oyewole, Ibitoye, Zaka, Alejo, and Alao (2024), working with the Nigerian Stored Products Research Institute Parabolic Shaped Solar Dryer for cowpea flour production in Ibadan, Nigeria, reported a return on investment of 64%, a benefit-cost ratio of 1.64, and a payback period of about one year and seven months. Outside Nigeria, Rulazi, Marwa, Kichonge, and Kivevele (2023) evaluated a solar-assisted heat pump dryer and reported payback periods of 2.6-3.0 years depending on the product dried, while Shigella, Tchicaya Loemba, Yawe, Kichonge, and Mwema (2026) combined IJEMT life-cycle assessment with techno-economic analysis of a solar-biogas hybrid banana dryer and reported a payback period of about 1.3 years alongside a substantially lower carbon footprint than solar-electric or solar-diesel alternatives. Taken together, these studies show that payback periods for solar and solar-hybrid dryers reported in the literature typically fall between about 0.7 and 3.0 years, which provides a useful benchmark against which the simulated economic indicators for the present design (Section 4.6) can be judged. Overall, the reviewed literature converges on four points relevant to this paper: (i) solar and solar–biomass dryers reliably cut drying time by roughly 30-75% relative to open-sun drying; (ii) single-term exponential (Newton/Lewis) and related thin-layer models are an accepted, simplified way of representing grain moisture-ratio decay during solar drying and are widely used alongside more complex multi-parameter models such as Page, Midilli, and Wang-and- Singh; collector and system efficiencies in the 12-80% range have all been reported depending on whether the dryer is passive/natural-convection or actively controlled/hybrid; and (iv) where economic analysis has been carried out, solar and solar-hybrid grain/food dryers have generally been found to be financially viable, with payback periods under three years. 3.0 Materials and Methods 3.0.1 Nomenclature The principal symbols used in the design equations of Section 3.2 are listed in Table 0 for ease of reference. Symbol Meaning Unit mp Mass of fresh product per batch kg md Mass of dry matter in the batch kg Mi, Mf, Me Initial, final, and equilibrium moisture content (w.b.) % Mw Mass of water to be removed kg Qevap Heat required to evaporate the design moisture load kJ or MJ Cb Bound-moisture correction factor dimensionless hfg Latent heat of vaporisation of water kJ/kg I Incident solar radiation (irradiance) W/m2 Ac Collector surface area m2 ηc, ηs, ηd Collector, system-drying, and overall dryer efficiency % Qu Useful heat gain rate of the collector W or kW UL Overall collector heat-loss coefficient W/m2·K Tc, Ta Collector-plate and ambient temperature °C t Drying time / drying period h σ Product loading density on drying trays kg/m2 At, Au, nt Total tray area, unit tray area, number of trays m2, m2, — MR Moisture ratio dimensionless k, n Thin-layer drying-rate constant and exponent h−1, dimensionless IJEMT Symbol Meaning Unit V, Qa Design air velocity and volumetric airflow rate through the bed m/s, m3/s Pf Fan/blower shaft power requirement W Co Estimated capital (fabrication) cost ₦ NPV, BCR, IRR, PBP Net present value, benefit-cost ratio, internal rate of return, simple payback period ₦, —, %, years Table 0: Nomenclature of principal symbols used in Section 3. 3.1 Design Basis and Assumptions The dryer is sized for a smallholder demonstration batch of 100 kg of freshly harvested maize, reduced from an initial moisture content of 20% to a safe storage moisture content of 13% (both wet basis), consistent with the moisture ranges reported by Asemu et al. (2020) and Akowuah et al. (2021). Design meteorological conditions were taken as representative of a typical dry-season day in the Bali/Jalingo area of Taraba State: peak solar irradiance of about 600-700 W/m2 around solar noon, and ambient temperature in the range 27-34°C, consistent with the order of magnitude reported for Nigerian solar dryer studies (Nwankwo et al., 2022; Uwa Ujunwa et al., 2024). A mixed-mode configuration (glazed flat-plate collector plus a separate drying chamber, with provision for a biomass back-up heater for early- morning or overcast conditions) was adopted, following the hybrid approach validated by Akowuah et al. (2021) and Ononogbo et al. (2024). 3.2 Governing Design Equations (a) Moisture content and mass of water to be removed. For a fresh product mass mp at initial moisture content Mi (w.b.) dried to a final moisture content Mf (w.b.), the dry-matter mass md and mass of water to be evaporated Mw are: md = mp (100 − Mi) / 100 (1) Mw = mp − [md / (1 − Mf /100)] (2) (b) Heat required to evaporate the moisture. The theoretical heat requirement Qevap accounts for the latent heat of vaporisation of water hfg and a correction factor Cb (typically 1.1-1.3) for the additional energy needed to remove bound moisture from the grain matrix: Qevap = Cb · Mw · hfg (3) (c) Useful heat gain and collector efficiency. For a collector of area Ac under incident solar radiation I, the useful heat gain rate Qu and instantaneous collector efficiency ηc are: Qu = ηc · I · Ac (4) ηc = Qu / (I · Ac) (5) (d) Required collector area. Equating the total useful heat delivered over the drying period t to the heat required to evaporate the target moisture gives the required collector area: Ac = Qevap / (ηc · I · t) (6) (e) System drying efficiency and overall dryer efficiency, adapted from the convention used by Uwa Ujunwa et al. (2024) and Ononogbo et al. (2024), with the bound-moisture correction factor Cb carried through for consistency with Equation 3: ηs = (Cb · Mw · hfg) / (I · Ac · t) = Qevap / (I · Ac · t) (7) ηd = ηs / ηc (8) (f) Tray and chamber sizing. For a design product loading density σ (kg product per m2 of tray), the required tray area At and number of trays nt of unit area Au are: At = mp / σ (9) nt = At / Au (10) IJEMT (g) Thin-layer drying kinetics. Moisture ratio MR is conventionally defined relative to the equilibrium moisture content Me: MR = (M − Me) / (Mi − Me) (11) For this simulation, the single-term (Newton/Lewis-type) exponential model was adopted for simplicity and transparency, following its established use as one of several accepted thin- layer models for solar-dried maize (Akowuah et al., 2021; Springer/Discover Food review literature on thin-layer solar drying models): MR = exp (−k · t) (12) where k (h−1) is an empirical drying-rate constant. Rearranging Equation 11 gives the moisture content at any time t: M(t) = Me + (Mi − Me) · exp(−k · t) (13) It should be noted that more elaborate multi-parameter models (Page, Midilli, Wang-and- Singh, and others) are frequently reported in the literature to give a closer statistical fit to experimental drying curves; the single-term model was adopted here deliberately, for transparency and ease of hand/spreadsheet calculation appropriate to a polytechnic project, and because the objective of this paper is illustrative design sizing rather than curve-fitting to a specific experimental dataset. (h) Collector heat loss. The useful heat gain of a flat-plate collector is more completely expressed by the Hottel-Whillier-Bliss relation, which nets off top, edge, and back conduction/convection losses (represented by an overall loss coefficient UL) from the absorbed solar energy: Qu = Ac · [I·(τα) − UL·(Tc − Ta)] (14) where (τα) is the transmittance-absorptance product of the glazing-absorber combination. Equation 14 was used only as a consistency check on the simpler efficiency form of Equation 4; because no physical collector has yet been built, UL and (τα) were not independently estimated, and Equation 4 (with an assumed lumped efficiency ηc) was retained as the primary sizing tool for this stage of the project. (i) Airflow rate and fan power. For a design air velocity V through the tray cross-sectional area Ax, the volumetric airflow rate Qa and the indicative fan/blower shaft power Pf (at a static pressure drop ΔP and fan mechanical efficiency ηf) are: Qa = V · Ax (15) Pf = (Qa · ΔP) / ηf (16) (j) Simplified economic evaluation. Following the cost-benefit framework used by Akowuah, Bart-Plange, and Dzisi (2021b) for a solar-biomass hybrid maize dryer, the net present value and benefit-cost ratio of the investment over an n-year evaluation period at discount rate r, given an annual net cash flow CF and capital cost Co, are: NPV = − Co + Σ [CF / (1 + r)^t], for t = 1 to n (17) BCR = [Σ CF / (1 + r)^t] / Co (18) and the simple payback period is: PBP = Co / CF (19) The internal rate of return is the discount rate r at which NPV in Equation 17 equals zero; it was obtained in Section 4.6 by iterative (trial-and-error) evaluation of Equation 17, consistent with standard engineering-economic practice. 3.3 Simulation Procedure (Important Note on Data Provenance) At the time of writing, a physical prototype of the dryer described in Section 3.1 had not yet been fabricated, and no field or laboratory measurements were available. Rather than fabricate figures and present them as experimental results, all performance data reported in Section 4 were generated by direct numerical evaluation of Equations 1-13 in a spreadsheet environment, using input values that are explicitly assumed and stated (design solar IJEMT irradiance, ambient temperature range, collector efficiency, drying-rate constants). This mirrors an accepted methodology in the reviewed literature, where simulation (including CFD-based simulation) is used to evaluate dryer performance ahead of, or in parallel with, physical testing (Ikwuagwu et al., 2024). The drying-rate constant adopted for the solar dryer (k = 0.175 h−1) was selected so that the simulated curve reaches 13% (w.b.) moisture content in 5 hours, reproducing the drying-time benchmark reported experimentally by Akowuah et al. (2021) for a solar-biomass hybrid dryer; the open-sun constant (k = 0.0583 h−1, i.e., one- third of the solar-dryer value) was selected to reproduce the corresponding 15-hour open-sun benchmark reported in the same study. Every table and figure in Section 4 is explicitly labelled 'simulated' and should be read as a design-stage estimate, not as a validated experimental result. Fabrication and field/laboratory instrumentation (pyranometer, k-type thermocouples, hot-air oven or digital grain moisture meter, hot-wire anemometer) to validate these simulated figures are recommended as the necessary next stage of the project (Section 5). 4.0 Results and Discussion 4.1 Design Sizing Calculations Applying Equations 1-10 to the design basis of Section 3.1 (mp = 100 kg, Mi = 20%, Mf = 13%, design irradiance I = 600 W/m2, assumed collector efficiency ηc = 0.45, drying period t = 5 h, tray loading density σ = 12 kg/m2) gives the summary design parameters in Table 1. S/N Design Parameter Symbol Calculated Value 1 Batch mass of fresh maize mp 100 kg 2 Dry matter mass md 80.0 kg 3 Mass of water to be removed Mw 8.05 kg 4 Heat required to evaporate moisture (Cb = 1.15, hfg = 2430 kJ/kg) Qevap 22.5 MJ 5 Design solar irradiance I 600 W/m2 6 Assumed collector efficiency ηc 45% 7 Required collector area Ac 4.86 m2 8 Collector length : width ratio L : W 1.5 : 1 9 Collector dimensions L × W 2.70 m × 1.80 m 10 Tray loading density σ 12 kg/m2 11 Total tray area required At 8.33 m2 12 Unit tray size Au 1.0 m × 0.7 m 13 Number of trays nt 12 trays Table 1: Simulated/calculated design sizing summary for the 100 kg-batch mixed-mode solar–biomass maize dryer. 4.2 Simulated Diurnal Radiation and Temperature Profile Table 2 presents the assumed/simulated hourly solar radiation and corresponding ambient, collector-outlet, and drying-chamber air temperatures for a representative dry-season day (9:00 h - 17:00 h). These values were used as inputs to, and are broadly consistent with the outputs of, Equations 4-8, and fall within the range of values measured in comparable IJEMT Nigerian solar dryer field studies (Uwa Ujunwa et al., 2024; Ononogbo et al., 2024; Njoku et al., 2025). Local Time Solar Radiation (W/m2) Ambient Temp. (°C) Collector Outlet Temp. (°C) Drying Chamber Temp. (°C) 09:00 390 27.5 34.0 31.0 10:00 520 29.0 40.5 36.5 11:00 630 31.0 48.0 43.0 12:00 690 33.0 53.5 47.5 13:00 700 34.2 55.0 49.0 14:00 640 33.5 51.5 46.0 15:00 540 32.0 46.0 41.5 16:00 410 30.0 39.5 35.5 17:00 300 28.5 33.0 30.5 Table 2: Simulated diurnal solar radiation and temperature profile (illustrative dry-season day, Bali/Jalingo area). Figure 1: Simulated diurnal solar radiation and temperature profile for the design day (bars = radiation; lines = temperatures). 4.3 Simulated Moisture Content Reduction Using Equation 13 with Me = 8% (w.b.), Mi = 20% (w.b.), k = 0.175 h−1 and k(open- sun) = 0.0583 h−1 (Section 3.3), the simulated moisture content of the maize batch over time is given in Table 3 and Figure 2. IJEMT Drying Time (h) Simulated MC — Solar–Biomass Dryer (% w.b.) Simulated MC — Open- Sun Drying (% w.b.) 0 20.00 20.00 1 18.07 19.32 2 16.46 18.68 3 15.10 18.07 4 13.96 17.50 5 13.00 16.97 6 12.20 16.46 9 — 15.10 12 — 13.96 15 — 13.00 Table 3: Simulated moisture-ratio-based drying curve — mixed-mode solar–biomass dryer versus open-sun drying. Figure 2: Simulated maize moisture content reduction curve, mixed-mode solar-biomass dryer versus open-sun drying. The simulation indicates that the design reaches the 13% (w.b.) safe-storage moisture content in approximately 5 hours, compared with about 15 hours under open-sun drying — a time saving of roughly 67%. This is consistent, in order of magnitude, with the 60-75% drying- time reduction reported for renewable-energy-based crop dryers in Nigeria by other authors (see Section 2), and with the threefold acceleration reported by Akowuah et al. (2021) for a solar-biomass hybrid dryer relative to open-sun drying of maize. 4.4 Simulated Efficiency and Energy Performance Table 4 summarises the simulated thermal performance indicators computed from Equations 4-8 for the sized collector (Ac = 4.86 m2) operating over the 5-hour design drying period. IJEMT S/N Performance Indicator Simulated Value 1 Collector (thermal) efficiency, ηc 45.0% 2 Total solar energy incident on collector over 5 h 52.5 MJ (14.6 kWh) 3 Useful heat delivered to drying air over 5 h 23.6 MJ (6.6 kWh) 4 Heat required to evaporate design moisture load 22.5 MJ 5 System drying efficiency, ηs 42.9% 6 Overall dryer efficiency, ηd (= ηs/ηc) 95.3% 7 Specific energy consumption per kg water removed ≈ 2,795 kJ/kg 8 Drying time reduction versus open-sun drying ≈ 66.7% (5 h vs 15 h) Table 4: Simulated thermal and drying-efficiency performance summary. The simulated collector efficiency (45%) and system drying efficiency (about 43%) fall within the wide range reported in the literature for solar/solar-biomass grain dryers, from about 10-12% for simple, unassisted natural-convection dryers (comparable to the low forced-convection efficiencies reported in early Nigerian paddy-dryer studies) up to 80% or more for actively controlled hybrid systems with heat recovery (Njoku et al., 2025). The mid- range value assumed here is consistent with a passive/mixed-mode design of the type reported by Nwankwo et al. (2022) and Uwa Ujunwa et al. (2024), and is considered a realistic, conservative planning figure for a first-generation workshop-fabricated unit at Federal Polytechnic Bali, rather than an optimistic best case. 4.5 Sensitivity of Collector Area to Design Assumptions Because the collector area (Equation 6) depends directly on the assumed collector efficiency and design irradiance, a simple sensitivity check was carried out by re-evaluating Equation 6 while holding all other inputs (Qevap = 22.5 MJ, t = 5 h) constant. Table 5 shows the required collector area for collector efficiencies between 35% and 55% at the design irradiance of 600 W/m2, while Table 6 shows the required area for irradiance values between 500 and 700 W/m2 at the design efficiency of 45%. Assumed Collector Efficiency, ηc Required Collector Area, Ac (m2) 35% 5.95 40% 5.21 45% (design value) 4.63 50% 4.17 55% 3.79 Table 5: Sensitivity of required collector area to assumed collector efficiency (I = 600 W/m2, t = 5 h). Assumed Solar Irradiance, I (W/m2) Required Collector Area, Ac (m2) 500 5.56 600 (design value) 4.63 700 3.97 IJEMT Table 6: Sensitivity of required collector area to assumed design irradiance (ηc = 45%, t = 5 h). The sensitivity check shows that under-estimating collector efficiency or irradiance leads to a larger, more conservative collector area, while over-estimating either parameter would under-size the collector and risk failing to reach the target moisture content within the design drying period. On this basis, the collector dimensions in Table 1 (2.70 m x 1.80 m, 4.86 m2) were deliberately set slightly above the bare minimum computed area (4.63 m2) — a margin of about 5% — to provide a safety allowance against cloudy-day irradiance shortfalls and glazing degradation over time. 4.6 Simulated Economic (Cost-Benefit) Evaluation A simplified, illustrative economic evaluation was carried out using Equations 17-19, following the cost-benefit framework of Akowuah, Bart-Plange, and Dzisi (2021b). All monetary figures in this subsection are assumed/estimated planning figures for illustrative purposes only, and are not fabrication quotations; they should be replaced with actual local bill-of-quantities figures once the unit is fabricated. A capital cost of ₦380,000 was assumed for materials (mild steel frame, glazing, insulation, trays, blower, biomass back-up chamber) and workshop fabrication labour, an annual maintenance cost of ₦25,000 was assumed, and a nominal per-batch drying service charge of ₦3,000 was assumed for an estimated 40 batches per drying season, giving an annual gross revenue of ₦120,000 and a net annual cash flow of ₦95,000. A discount rate of 15% (representative of prevailing Nigerian lending rates used in similar studies) and a 10-year evaluation period were assumed. Table 7 summarises the results. S/N Economic Indicator Simulated Value 1 Estimated capital cost, Co ₦380,000 2 Assumed annual maintenance cost ₦25,000 3 Assumed annual gross revenue (40 batches × ₦3,000) ₦120,000 4 Net annual cash flow, CF ₦95,000 5 Simple payback period, PBP (Eq. 19) ≈ 4.0 years 6 Net present value, NPV at r = 15%, n = 10 y (Eq. 17) ≈ ₦96,800 (positive) 7 Benefit-cost ratio, BCR (Eq. 18) ≈ 1.26 8 Internal rate of return, IRR ≈ 21.4% Table 7: Simulated cost-benefit indicators for the proposed workshop-fabricated unit (illustrative planning figures). The positive simulated NPV and a BCR above 1.0 indicate that the design is, in principle, economically viable at the assumed cost and utilisation levels, though the simulated payback period of about 4 years is longer than the roughly 0.7-3.0 years reported for larger-capacity or commercially operated solar/solar-hybrid dryers in the literature reviewed in Section 2.3 (Akowuah et al., 2021b; Nwakuba et al., 2020; Jimoh et al., 2024; Rulazi et al., 2023; Shigella et al., 2026). This difference is expected and is attributed to the smaller scale and lower assumed utilisation (batch throughput) of a single workshop/demonstration unit relative to the commercially oriented or larger-capacity dryers reported in those studies; a higher utilisation rate (more batches per season, or shared/cooperative use by several farmer households) would shorten the simulated payback period substantially, and this is recommended as a specific variable to measure once the unit is fabricated and field-tested. IJEMT 4.7 Comparison with Published Solar Dryer Performance Table 8 places the simulated performance indicators of the present design alongside selected experimentally validated results reported in the 2020-to-date literature reviewed in Section 2, to give a sense of whether the simulated outputs are within a realistic range. Study / Source Dryer Type Key Reported/Simulated Performance This study (simulated) Mixed-mode solar-biomass, maize, 100 kg batch 5 h to reach 13% w.b.; ηc ≈ 45%; ηs ≈ 43% Akowuah et al. (2021a) Tractor-mounted solar-biomass hybrid, maize 19% → 13% w.b. in 5 h (vs 15 h open-sun) Asemu et al. (2020) Solar bubble dryer, maize (thin layer) ~24 h to reach 13% w.b.; thin layer faster than thick Ntwali et al. (2021) Inflatable solar dryer, maize Below 14% w.b. within 2 days; ~7°C rise over ambient Uwa Ujunwa et al. (2024) Natural-convection tunnel dryer, maize/tomato/mango 30-50% drying-time reduction vs open sun; 25-73°C chamber Ononogbo et al. (2024) Hybrid solar-electric vegetable dryer Chamber temp. rise 35.9°C → 58.1°C (no-load) Njoku et al. (2025) Smart dual-energy crop dryer with heat recovery Collector thermal efficiency ≈ 80.8% Tesfaye et al. (2022) Solar tunnel dryer, ginger Lower-bound natural-convection efficiency reference Table 8: Comparison of the present study's simulated performance with selected 2020-to-date literature (values as reported by the respective authors). The comparison shows that the simulated 5-hour drying time and mid-range (43-45%) efficiency values for the present design sit comfortably within the range of experimentally validated results reported for comparable passive/mixed-mode solar and solar-biomass maize dryers, reinforcing that the design sizing in Section 4.1 is neither unrealistically optimistic nor overly conservative. 4.8 Relevance to Taraba State and Recommended Next Steps Taraba State's economy is strongly agriculture-dependent, and the state has been the focus of recent agro-climatic resilience programming aimed at reducing farmers' exposure to rainfall variability and heat stress. A low-cost, largely locally fabricable solar–biomass grain dryer of the type sized in this paper directly supports that resilience objective: it reduces a smallholder household's dependence on unpredictable open-sun drying weather windows, cuts postharvest grain loss and mould/aflatoxin risk, and does not require grid electricity or imported fuel. Because the design uses standard mild-steel, timber, and glazing materials similar to those reported by Nwankwo et al. (2022) and Uwa Ujunwa et al. (2024), it is well suited to fabrication in the Federal Polytechnic Bali Agricultural/Mechanical Engineering workshops as a final-year or HND project, after which field instrumentation should be used to validate the simulated figures reported in Section 4 against real measured data. 5.0 Limitations of The Study This study is a design-stage engineering analysis and should be read with the following limitations in mind. First, and most importantly, no physical prototype has yet been fabricated IJEMT or tested; every numerical result in Section 4 is a simulated estimate derived from Equations 1-19 under stated assumptions, not a measured value, and real performance may differ once local material properties, workmanship quality, and actual weather conditions are accounted for. Second, the single-term (Newton/Lewis) thin-layer model adopted in Equation 12 is a simplification; it does not capture the initial constant-rate drying period observed in some experimental studies, nor variable ambient humidity effects, and a multi-parameter model (Page, Midilli, or Wang-and-Singh) fitted to real experimental data would likely give a closer representation of the true drying curve, as noted in Section 3.2(g). Third, the collector efficiency ηc was treated as a constant lumped value for simplicity, whereas in reality it varies through the day with irradiance, ambient temperature, and wind speed, as reflected in the more complete Hottel-Whillier-Bliss formulation (Equation 14), which was not fully parameterised in this study. Fourth, the economic evaluation in Section 4.6 uses assumed, illustrative Naira figures for capital cost, utilisation, and service charges rather than a locally sourced bill of quantities, and should be revisited once actual fabrication costs are known. Finally, biological aspects of drying quality (aflatoxin risk, germination viability for seed maize, colour and nutrient retention) were outside the scope of this design paper and would need to be assessed experimentally, as done by, for example, Ntwali et al. (2021) for aflatoxin content. Notwithstanding these limitations, the design and simulation approach adopted here is consistent with accepted engineering practice for early-stage sizing of solar drying systems, as illustrated by the CFD-based simulation approach of Ikwuagwu et al. (2024). 6.0 Conclusion and Recommendations This paper has presented the design theory, sizing calculations, and a clearly labelled simulation-based performance evaluation of a mixed-mode solar–biomass grain dryer for a 100 kg smallholder maize batch, intended as a project/demonstration unit for the Department of Agricultural Engineering, Federal Polytechnic Bali. The sizing calculations yielded a collector area of about 4.9 m2 and a 12-tray drying chamber, while the simulation (explicitly based on assumed design inputs rather than measured data) indicated a drying time of about 5 hours to reach safe storage moisture content, versus about 15 hours for open-sun drying, at a collector efficiency of about 45% and a system drying efficiency of about 43%. These simulated outcomes are consistent, in order of magnitude, with experimentally validated solar maize dryers reported in the 2020-to-date literature reviewed in Section 2. It is recommended that: (i) the design be fabricated in the Polytechnic's engineering workshop using locally available mild steel, timber, and glazing materials; (ii) the fabricated unit be instrumented with a pyranometer, k-type thermocouples, a hot-wire anemometer, and a digital or oven-method grain moisture meter to obtain real experimental data; the simulated drying curves and efficiency values reported in this paper be validated, and the single-term drying model be compared against Page, Midilli, and Wang-and-Singh models using the resulting experimental data, following established practice in the literature; and (iv) consideration be given to extension/outreach demonstration of the validated unit to smallholder farmer groups within Taraba State as a low-cost, climate-resilient postharvest technology. IJEMT References Abulude, I., & Wahlen, S. (2024). Food loss analysis in Nigeria: A systematic literature review. 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