1 Introduction

Industrial symbiosis is firmly establishing itself as a circular economy operational tool for the implementation of the energy transition at various levels throughout the world [1, 2]. It deals with the recycling of waste materials and energy generated during a production process to replace conventional production inputs during other conventionally disengaged processes, whether they are owned by the same company or different companies [2, 3].

The circular economy is also characterized as an industrial system that is consciously restorative or regenerative [4, 5]. As opposed to the “end-of-life” concept, it strives to restore value, moves toward the use of renewable energy, detests the use of toxic chemicals that prevent reuse, and seeks to eliminate waste through the superior design of materials, products, and systems [6, 7]. These sustainable strategies could lessen environmental degradation by reducing the demand for virgin materials and releasing pressure on ecosystems that support climate adaptation, such as forests that control temperature [8, 9].

On the flip side, among the problems brought about by the economic development of emerging economies is the significant amount of waste that is produced and the resources that are not recycled in industrial activities [5, 10]. The biggest environmental minefields associated with waste pollution management is its aggregation to guide decisions on investments in waste circularization and industrial symbiosis such as the construction and long-term sustainability of biorefinery. Also, the need for firewood to provide energy to cook continues to grow due to society's unsustainable consumption patterns, which are also climate, biodiversity, health and economic threat [11, 12].

Marrying the above trigonal scenarios to simulate the case of Ghana, solutions can be provided by capitalizing on the millions of agro-based wastes generated in the country to upheave the cooking energy security issues in the country. The excessive bio waste streams available includes but not limited to (a) coconut husk [13], (b) sawdust [14], (c) palm kernel shell [15], and (d) cocoa pod [16] which can be converted into useful energy products as shown in Fig. 1. This energy-based industrial symbiosis business models have been tried and tested in other nations, including Kenya and Uganda [17, 18], as a double-edged strategy to increase job creation for youth and provide briquette cooking options for households that still use dirty fuels.

Fig. 1
figure 1

Waste samples generated within agro-based industries a Coconut husk b sawdust c palm kernel d cocoa pod

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However, one of the limitations associated with waste-based briquettes have identified with reduction in efficiency when stored for a long period of time under varied storage conditions and other transportation related issues [7, 19]. Ideally, the densified material must not be physically harmed between manufacture and use, which is a significant issue with this type of application [20]. It is one of the factors that affects product standardisation because it can cut down on transportation expenses and lower the risk of fire from briquette dust explosions [21]. The shelf life of briquette has been reported to affect the quality and overall efficiency [22]. Briquettes significantly lost its bulk density by 23.6% after being stored for 9 months, according to [23]. Additionally, [24] discovered that after 2 months of storage, briquettes had a bulk density reduction of 11% and a calorific value reduction of about 5%. Other elements also become more favourable, such as relaxed density, which directly affects the hygroscopic characteristics of briquettes and flue gas emissions [6, 25].

Hence, an experiment within the geographical area of the briquette development and use is very important to improve the knowledge of manufacturers and researchers in the field in order to assess the impact of the storage time and ambient conditions on the calorific value and mechanical resilience. Even though the shelf life of the briquette plays an instrumental role in determining its quality, and marketability, there still exists a huge gap in literature in terms of the several feedstocks available for densification. As a case study, current work is limited to the investigation of the changes in quality that may occur when oil palm waste is used as a briquette without observing similar outcomes for the remaining waste resources sampled in this study. Palm kernel shell (PKS) and palm kernel decanter cake (PKDC) are used as the primary biomass material and card board pulp as binder.

The analysis is focused on cardboard waste discarded from a local grocery shop mixed with PKS and PKDC, discarded from local palm kernel oil processing industries. Cardboard waste is a discarded resource that is mostly disposed of in the local landfill while the PKDC is left on the processing site in heaps while PKS is combusted at its raw state in a three-stone stove by the cooks. Therefore, adding value to these waste resources for energy production is a good alternative sustainable cooking fuel option. The novelty in the current study is highlighted through the use of blended PKDC and PKS as feedstock. To the best of our knowledge, this is also the first study to use response surface modelling to optimise storage conditions and shell life of briquette within the sub-Saharan Africa region. The study investigates the mechanical properties and calorific values of briquettes made from the PKS and PKDC that have been stored for 180 days. The methodology in this work can be extended to other feedstocks within the region.

2 Methodology

2.1 Densification of raw materials

Further aggregation of waste resources available in Ghana was made based on data obtained from literature. Oil palm waste (PKS and PKDC) was selected for the experimental phase of the study. The choice of the feedstock selected was based on the fact that the Central Region of Ghana, where the study was conducted is one of the key producers of oil palm within the country [21]. Following the protocols and mixing ratio presented in [26], an 80% blend of palm kernel shell and decanter cake was chosen as the feedstock and 20% cardboard waste material as an adhesive for this study.

To create uniform particle sizes and establish homogeneity, the charred kernel shells were pulverized and processed through a 2 mm sieve. Old cardboard boxes from a grocery store were collected and ripped into manageable pieces [19]. It was immersed in water for 30 min, then blended for a minute at 2000 rpm to produce a smooth pulp with 3.2 mm sized particles on average. To make the paste smoother, it was whisked for an additional 10 min. The bentonite clay, on the other hand, was crushed using a mortar and pestle to produce particles that were, on average 21 µm in size. The cardboard binder was adopted from [27]. The materials were then densified with a caulking gun at a pressure of 10 MPa and sun dried for 30 days. In Fig. 2, a flow process of waste generation to the final usage of the briquette is presented to visualise the circularisation and industrial symbiosis process demonstrated in this study.

Fig. 2
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Flow process of waste generation

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2.1.1 Preparation of materials

See Fig. 2

2.1.2 Calorimetry

The Cologne Institute for Renewable Energy’s bio-energy lab at the Faculty of Process Engineering, Energy and Mechanical Systems at the TH Köln University of Applied Sciences carried out this measurement in accordance with European standards. Using a bomb calorimeter that satisfies the following criteria Kern, 770 (Range: 0.0005–220 g and Accuracy: 0.0002 g) and IKA, C 200 micro balances (mode: isoperibol, duration: 17 min). The grams of the biomass sample's weight were determined. A micro balance was used to weigh an empty crucible. Once the sample was inside, it was weighed again to determine the sample’s mass. A threat was attached to the ignition wire. The sample was attached to the crucible, which was then set up in a holder for additional analysis.

2.2 Storage conditions

The briquettes were divided into two groups (class 1 and class 2) and stored for a total of 180 days, from 10th March 2022 to 6th September 2022, under two different storage conditions. Figure 3 illustrates how class 1 briquettes were stored in the lab at room temperature in a plastic rubber while class 2 briquettes had no packaging or rubber. A presumption is made regarding the temperature conditions that prevailed during the storage period.

Fig. 3
figure 3

Conditions for briquette storage

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2.3 Mechanical properties

2.3.1 Durability index

The durability index was calculated using the protocols established in [11]. The briquette was dropped to the ground from a height of three feet in accordance with ASTM standard D440. Equation 1 was used to determine the final percentage loss:

$${\text{Total weight loss}}\left( {{\text{Wt}}} \right) = \left\lfloor {\left( { \delta a - \delta b} \right) / \delta a} \right\rfloor \times 100$$
(1)

where \(\delta a\) is the initial briquette weight, as seen in Fig. 4 and \(\delta b\) is the weight after the briquette has cracked into fragments. The percentage durability index (\(\delta s\)) of the briquette is then determined by \(100\%-Wt\)

Fig. 4
figure 4

Equipment for mechanical test

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2.3.2 Relaxed density

Following the study by [12], the relaxed densities of the various samples were assessed using Eq. 2. Three different cross-sections of the briquettes were used to measure their mass, length, and diameter.

$$\rho r = \frac{{108000 \times W\left( {kg} \right)}}{{\pi \left[ {di + d\mu + d\varepsilon } \right]^{2} \times \left[ {Si + S\mu + S\varepsilon } \right]}}$$
(2)

where \(di, d\mu \; \text{and} \; d\varepsilon\) are the respective diameters taken in (m), while \(Si,\, S\mu \; \text{and} \; S\varepsilon\) are the lengths of the briquettes measured in (m) at different points and \(W\) denotes the weights of the briquettes.

2.3.3 Resistance to impact

The resistance to impact was carried out in accordance with [13] and ASTM D440, as seen in Eq. 3. Until a fracture formed, the briquettes were allowed to fall freely seven times from a height of 3 feet onto a tiled floor. After the seven drops, the weight of the briquette’s fragments was calculated. Parts that weighed 5% or more than the weight of the initial mass of the briquette piece were used to calculate the resistance to impact, as shown in Eq. 3.

$$\infty R = \frac{N\sigma }{{N \cup }} \times 100$$
(3)

where \(N\sigma\) represents the number of drops and \(N\cup\) represents the number of fragmented parts that weighed 5% or more of the actual weight of the briquette after \(N\sigma\).

2.3.4 Hygroscopic property

The briquettes were submerged for 30 s in a beaker of water that was 30 mm deep and room temperature as seen in Fig. 4. By doing this, it was possible to calculate how much water the briquette absorbed while submerged in the liquid. The Eq. 4 was then used to calculate the percentage of water gained. Briquettes gain

$$\frac{\beta - \alpha }{{\beta }} \times 100$$
(4)

where \(\beta\) represents the final weight of the briquette and \(\alpha\) is the actual weight of the briquette.

2.4 Mathematical modelling and optimization

To calculate the impact of shelf life on mechanical properties, response surface modelling (RSM) was used. Data for response surface modelling includes response (dependent) and factor (independent variables). In this study, the response variables included calorific value, shatter index, relax density, resistance to impact, and hygroscopic properties, while the independent variables included open air storage and rubber packaged storage. Design Expert 13 software was used for the RSM modelling and optimization. ANOVA and the significance test for lack of fit were used to further test the model for each response variable. Following that, contour plots were used to graphically illustrate the response equations. In the Table 1, The coded values were designated by − 1 (minimum), 0 (centre), + 1 (maximum), − α and + α. Equation 5 the mathematical representation.

$$\beta _{0} + \sum\nolimits_{{i = 1}}^{3} {\beta _{i} } X_{i} + \sum\nolimits_{{i = 1}}^{3} {\beta _{{ii}} } X_{i}^{2} + \sum\nolimits_{{i = 1}}^{3} {\sum\nolimits_{{j = i + 1}}^{3} {\beta _{{ij}} } } X_{i} X_{j}$$
(5)
Table 1 Factors for central composite design for briquette production
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2.5 Considerations for uncertainty in model

The uncertainty factor taken into account in the present work is physical variability. This type of uncertainty was referred to as aleatory and may result from the inherent randomness or natural variability of physical processes and variables as a result of numerous factors, including operational and environmental variations, construction processes, and quality control. This kind of uncertainty can be found in both internal aspects of the system (like material strength, porosity, and geometry variations) as well as external factors like binder ratio, biomass concentration of the materials, temperature, and humidity. This resulted in differences in the exact values of the model parameters, especially when the procedure was repeated by others.

3 Results and discussion

3.1 Making a case for circular economy and industrial symbiosis

After combining through academic literature, the resources can be efficiently aggregated by summing up the volume of waste resources that are shortlisted above. [11, 28] estimated that about 97,100,000 kg of sawdust are produced in the nation each year, but only 60% of that is recycled; the remainder is deemed waste. This scenario implies that using composite sawdust for clean energy generation via the circular economy concept will reduce waste generation in the saw milling industry.

Ghana also ranks as the 14th-largest producer of coconuts in the world, producing 507 000 metric tons of the nuts annually while employing close to 78,000 people [10]. The coconut fruit's husk and shell are carelessly disposed of as solid waste and set on fire in areas that are accessible to the general public, such as sidewalks, gutters, and backyards. The average weight of the husk is 1.14 kg, compared to 1.5 kg for the entire fruit, according to [24, 29], which translates into a waste resource of 385.514 Mt/year To address the current energy shortage among fishmongers, briquette manufacturing industries can be established along Ghana’s coastal belt.

The oil palm business is another promising area that has the potential to improve the country’s cooking energy difficulties in the next years. According to Asante [30], Ghana produces around 2.4 million tonnes of oil palm per year. The fronds, empty fruit bunch, shell, decanter cake, and fiber can yield around 278,896.4 Mt waste [12, 31, 32]. Considering these numbers, it is possible to fully recover the energy contained in this particular biomass and use it to power hundreds of rural Ghanaian homes. In addition, the oil palm industry has the potential to create job opportunities for rural communities, further contributing to the country’s economic development. By utilizing the waste materials from oil palm production, Ghana can reduce its reliance on traditional cooking fuels like wood and charcoal, which often contribute to deforestation and environmental degradation. Implementing these sustainable practices in the oil palm industry can not only address the issue of cooking energy but also promote a greener and more sustainable future for Ghana.

As resource aggregation scouting continues, cocoa waste is another potential source of raw materials for the briquetting industry. The skin is the largest part of the cocoa pod, making up 73.73% of the fruit and about 651,515.1 Mt/year cocoa waste available each year for circularisation [13, 33, 34]. As a result, energy can be extracted from the waste produced by just these sectors and transformed into cooking fuels for use in homes. Furthermore, the implementation of a circular economy in the agro-based waste sector will also create new job opportunities in waste management and renewable energy sectors, promoting inclusive economic growth and decent work. Moreover, by reducing the burning of waste in open fields, this approach will improve air quality and reduce respiratory problems, thus contributing to better health and well-being for communities. Lastly, by reducing the reliance on raw biomass and promoting waste-to-energy technologies, the circular economy in the agro-based waste sector will contribute to mitigation of climate change.

3.2 Optimal shatter index

Packaged storage (A) and open air storage (B) are the two distinct variables, and each is assessed at one of three different coded levels: lower (1), middle (0), and higher (+ 1). According to the results of the ANOVA for the response surface, a quadratic model was found for the shatter index, with an F-value of 32.66 indicating that it is significant. Both A and B are significant model terms in this situation, as shown in Eq. 6. The predicted R2 value of 0.7529 is in reasonable agreement with R2 of 0.8672 and Adj R2 of 0.8407, indicating that the difference is less than 0.2. The significance of the model terms and the agreement between the predicted and actual R2 values do not necessarily imply that the quadratic model accurately represents the relationship between the coded levels and the shatter index. It is a well-known fact that the Adeq precision works best with a signal to noise ratio of at least 4. A good signal was obtained in the current study, yielding a ratio of 23.36, indicating that this model can be used to explore the design space. The Eq. 6 allows for the calculation of the shatter index based on the ANOVA parameters. This equation provides a quantitative measure of the shatter index, which can be used to evaluate the quality of the design space. Furthermore, the statistical significance of the shatter index suggests that it is a reliable indicator for assessing the effectiveness of the model. Therefore, Eq. 6 can be utilised in future studies to optimize the design and improve the overall performance of the system.

$$98.7553 - 14.761A - 14.0938B + 12.03AB - 7.04741A^{2} - 5.0716B^{2}$$
(6)

Figure 5 displays the shatter index contour plot and 3D response surface plot as a function of storage conditions and storage time. Five centre points were used for the modeling using Table 2. On days 30 and 105, respectively, the minimum and maximum values of the shatter indices for both samples within the predictive model were 43.22% and 100%. However, the results of the actual experiment showed that the open-air storage's minimum and maximum shatter indices of 72.34% and 100%, respectively, these were achieved on days 30 and 85. Throughout the remaining days of the experiment, the 100% shatter index persisted. This result contrasts with that of Gan et al. [35], who discovered that when mustard stalk biomass and molasses binder were used to make briquettes in India, the shatter index decreased. The shatter index decreased from 98% to 96.8% after 180 days of storage, or a 1.2% decrease. This might be due to the different briquette composition used in the study. The cardboard binder used in the studies may be the cause of the increase in shatter indices from this study. Due to the presence of lignin, the recyclable cardboard used in this study improves the briquette’s ability to bind, which is a well-known phenomenon.

Fig. 5
figure 5

Contour plot and 3-D surface plot for shatter index

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Table 2 Optimal values from RSM modelling
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Although the previous study’s work was not done at a specific time of year to account for temperature variations, it’s possible that this is what led to the opposite outcomes. It is possible to confirm the link between variations in operating temperature and the mechanical properties of briquettes using the finding Antwi-Boasiako and Acheampong [36]. According to the previous study, the extensive briquette pore destruction and the enlargement of briquette cracks lead to a decrease in mechanical strength at higher temperatures. Based on an analysis of the experimental work and statistical data, the composite briquettes in the current study should be stored for at least 85 days in order to maintain their optimal shatter indices.

3.3 Optimal relax density

The p-values for the model’s significant terms are less than 0.05 based on the solution to Eq. 8. The developed models are able to achieve the required statistical validity thanks to the values of the regression coefficients—0.955 for R2, 0.92 for modified R2, and 0.913 for predicted R2. The developed quadratic model’s value of 30.07 is above average (4). The high values of the regression coefficients, particularly R2 and modified R2, demonstrate a strong relationship between the independent and dependent variables. Additionally, the predicted R2 value suggests that the models can effectively estimate future outcomes. The above-average value of the quadratic model further supports its effectiveness in capturing the complexity of the data for the determination of the optimal relax density. Furthermore, the ANOVA results confirm that the statistical parameters obtained are within acceptable limits, allowing for confident exploration of the design space. Moreover, the good signal to noise ratio with a precision of 20.30 indicates that the models' predictions are consistent and reliable, further enhancing their practical utility.

$$1100.47 + 19.2328A - 91.529B - 10.25AB + 460.514A^{2} - 566.046B^{2}$$
(7)

The contour and 3D-surface models in Fig. 6 were developed to further analyse the interactions between the chosen independent variables and the relaxed density. These models allowed for a visual representation of the data, making it easier to identify any patterns or trends. The range of relaxed density varied depending on the factors being examined. These variations in relaxed density suggest that the factors being examined have a significant impact on the overall density of the data. It is interesting to note that for factor B, the relaxed density gradually increased over time, reaching its highest point on the 105th day at 1140 Kg/m3. Conversely, factor A showed a more fluctuating pattern, with the highest and lowest values occurring on the 30th and 180th days as 1102 Kg/m3 and 870 Kg/m3. respectively. This indicates that the effects of the factors on relaxed density may differ depending on the specific time frame.

Fig. 6
figure 6

Contour plot and 3-D surface plot for relax densities

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The results of the experiment indicate, as shown in Table 3, that the relaxation density tends to decline with increasing storage time. This outcome is consistent with the [37] discovery. According to the earlier study, after being kept for 9 months, briquettes significantly lost density, dropping by 23.6%. To make reference to [30], the density of briquettes made from wood waste and molasses decreased from 780.40 Kg/m3 to 729.40 Kg/m3 when stored for a long period of time. The reduction relax density has a correlation with the reduced moisture content of the briquette which in turn affects ease of ignition positively [38]. Longer storage therefore reduces moisture content, which in turn lowers smoke emissions during combustion. Correspondingly, by reducing the mass, briquettes' travel weight can also be decreased. This reduction in travel weight can lead to cost savings in transportation and handling. Furthermore, the decrease in density can also improve the handling and storage of the briquettes, making them easier to stack and store.

Table 3 Experimental data on the shelf life and storage conditions
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3.4 Optimal resistance to impact

The resistance to impact models created in this study were evaluated for significance using F-values, p-values, coefficients of determination, and the lack of fit test. The estimated F-values for the regression model were found to be 63.86 (< 0.0001) along with the p-values. The significant fitted models can explain the favourable correlation between independent factors and response. The accuracy and significance of the predictions made by the models regarding the ideal briquettes' resistance to impact were evaluated using the values of the coefficient of determination (R2 and Adjusted R2), which represent accuracy, sufficiency, and broad applicability of the models. R2 values for the quadratic Eq. (8) are 0.891 for the predicted R2, 0.963 for the modified R2, and 0.9785 for the modified R2. Equivalently, the high R2 values, particularly for the modified R2, suggest that the model is able to capture a large portion of the variability in the data, further supporting its reliability and usefulness in navigating the design room. Equation 8's design room can be navigated using the model because of the model's adequate precision of 26.4105. This level of precision allows for accurate predictions and analysis within the design room.

$$530.98 - 17.7248A + 1.12822B - 0.725AB - 29.972A^{2} - 35.419B^{2}$$
(8)

Figure 7 depicts the interaction effect of storage period on the impact to resistance for the two different conditions. The resistance to impact increased as storage time increased for both factors. A minimum and maximum value for packaged storage was found to be 532% on the 30th day and 712% on the 180th day, respectively, whereas for open air storage, 621% and 895% were found for the designated measurement days. This demonstrates that open air storage was more durable than packaged storage; the values increased with longer periods of storage. It is common to experience an increase in resistance to impact [20]. Previous researchers discovered that storing briquettes in open air for 20–35 days increased impact resistance by 60–68%. This suggests that the longer the paper briquettes are stored in open air, the stronger their resistance to impact becomes. These findings are significant as they highlight the potential benefits of open-air storage in improving the durability of paper briquettes. Also, the substantial increase in impact resistance observed in this study further validates the previous research findings, reinforcing the notion that extended open air storage is an effective method for enhancing the strength of paper briquettes.

Fig. 7
figure 7

Contour plot and 3-D surface plot for resistance to impact

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3.5 Optimal hygroscopic property

All of factors, A, B, and AB had an impact on the hygroscopic properties for this observation. The overall p-value for the model was less than 0.0001, demonstrating that the water resistance result has a satisfactory level of validity and consistency. It is clear that for the hygroscopic property response to be optimized, cohesion between the days of storage under the two conditions is necessary. The statistical test yielded a high regression coefficient of 0.966 with a modified value of 0.941, which is reasonably consistent with the value of 0.871. The model's suitability was demonstrated by the coefficient of variance (CV) and the 26.32 Adeq Precision. The coefficients of determination values of R2 0.9475, adjusted R2 values of 0.9002, and predicted R2 values of 0.802. Equation 9 shows the final optimization equation.

$$95.1041 - 17.2386A + 7.3257B + 13.5575 AB + 9.18073A^{2} - 2.13956 B^{2}$$
(9)

The ideal hygroscopic properties for both class 1 and class 2 were found on day 180, which is consistent with the actual experimental data. In Fig. 8 However, one of the limitations associated with waste-based briquettes have identified with reduction in efficiency when stored for a long period of time under varied storage conditions, the hygroscopic property increases over the course of storage; for the class 1 sample, this increase occurred between the 30th and 180th days of the experiment, going from 97.33 to 118%. Furthermore, the hygroscopic properties of the open-air storage were found to significantly increase from 98.26% on the 30th day to 143% on the 180th day. The resistance to water in the wood waste and distiller’s dry grain briquette developed by [30] increased from 123.4 to 184% after 180 days of storage. Increasing the hydrophilicity of palm kernel shell and decanter briquettes over a longer period of time could increase their resilience during storage and transportation [37]. Processing, shipping, storage, and combustion are made challenging by high affinity water [38].

Fig. 8
figure 8

Contour plot and 3-D surface plot for hydroscopic property

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3.6 Optimal calorific value

ANOVA for the optimal calorific indicated a p-value of 0.0003, and F-value of 23.05. Based on the R2 and adjusted R2 value, the quadratic equation was considered for the prediction of durability in the current study. The regression equation for the durability response in Eq. 10. The model had R2 and adjusted R2 values of 0.9427 and 0.9018, respectively and a predicted R2 of 0.8697. The three-dimensional graph and contour plots indicate the effect of the shelf life on the storage period and conditions under storage. The results from the model was consistent with the experimental results in terms of the declination trend. Equation 10 shows the mathematical representation for the optimal calorific value for the briquettes in current study.

$$25.3198 - 0.366A - 5.260B - 0.649AB - 13.7142A^{2} + 11.036B^{2}$$
(10)

However, for the 30th day, the predictive model produced values that were slightly higher than the experimental results in Table 3. The actual results for factors 1 and 2 were 27.61 MJ/Kg and 27.78 MJ/Kg, respectively. On the contrary, the data recorded in the predictive model on the 180th day in Fig. 9 was lower than the experimental data. With a difference of 31.40% and 12.99% between classes 1 and 2. The observation of a decrease in calorific value is consistent with [34]. A previous study found that after 180 days of storage, calorific value decreased by 9%.

Fig. 9
figure 9

Contour plot and 3-D surface plot for calorific value

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A reduction in calorific value is an unfavourable feature of solid biofuels. To ensure that the briquette has the highest possible calorific value, all manufacturers must consider the conditions under which the final product is stored. The samples kept in the plastic bag performed better in terms of sustained energy value. Although briquettes are priced by mass or volume and ease of handling, market forces determine the price of each fuel based on its energy content [39]. As a result, CV can be used to determine how competitive a processed biomass fuel is in a given market. Nonetheless, the cost of making briquettes has nothing to do with their CVs.

4 Conclusion

After 180 days of storage, the calorific value of briquettes declined in both cases. The tests demonstrated that the storage conditions of briquettes affect their mechanical strength. Briquettes stored in firmly sealed plastic bags and those not kept in plastic bags both changed physically after 180 days. It can therefore be concluded that briquettes must be placed within properly sealed plastic packages if producers are providing briquettes in bulk. Only in this manner will their required properties be ensured, even after 180 days of storage. This highlights the importance of carefully monitoring and controlling the moisture content during the packaging process to ensure the long-term integrity of the briquettes in future studies. Due to the fact that both conditions persisted in a climate that was very similar to that of the West African nations, those nations may apply the research methodology used in this study to their own specific geographical regions. In order to make better choices regarding the shelf life and storage conditions of briquettes, future research may consider the rate of decline in the calorific value and mechanical value of various biomass resources at different temperatures. In conclusion, there are a number of benefits from an economic and developmental standpoint when the circular economy and industrial symbiosis are combined. Replicating these ideas can therefore increase the share of renewable energy in developing nations’ energy mix.