Juan Franciosi
| Annie Vidarte* | Abraham Dávila
© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Fire incidents represent a substantial threat to the sustainability of companies, with their impact predominantly influenced by fire load density. This study aimed to examine the impact of plant layout dimensions on the overall fire load density within the rice milling industry of Lambayeque, Peru. To this end, the study characterized the sizing of four plants, determined the unit fire load of each work area, and employed simulation to analyze the relationship between area size and total fire load density in a rice plant layout. The findings indicated that (i) by increasing the size of the maneuvering yard areas by 4% to 10%, the fire load was reduced by 2% to 3%; and (ii) by increasing the size of the husk rice warehouses (by 10% to 21%), finished product (by 13% to 19%), and empty sacks (by 14% to 22%), the fire load increased by 3% to 8%, 2% to 4%, and 1% to 3% respectively.
fire, plant layout, fire load density, rice milling industry, fire load calculation model, scenario analysis
An environmental problem is characterized by a disruption in the natural equilibrium of various environmental components, including atmospheric air, hydrospheric water, and lithospheric soil. Such disruptions may result from accidents or disasters. Specifically, in the event of a fire, atmospheric air is negatively impacted by the release of toxic emissions [1], as well as by the generation of debris and non-recoverable solid waste [2].
When a building is impacted by fire, sustainability—defined as the comprehensive consideration of economic, social, and environmental dimensions of all activities—is compromised [3]. This results in environmental impacts, such as pollution and the generation of non-recyclable materials, social impacts, including job losses and service interruptions, and economic impacts related to remediation costs. Therefore, fire risk in buildings must be anticipated in the pursuit of sustainable societal development [4, 5]. Additionally, assessing the level of fire risk in properties necessitates adherence to mandatory protection and prevention requirements to mitigate such risks [6].
The predominant threat to human life during a fire incident is smoke inhalation, which is responsible for approximately 75% of fire-related fatalities, while the remaining 25% result from burns due to thermal exposure [7]. The building environment must be designed to ensure the availability of sufficient water sources, such as hydrants, sprinkler systems, wells, water tanks, or other fire-extinguishing agents, to facilitate firefighting operations, contingent upon the assessed level of risk [4-7]. Fire risk is contingent upon the various areas within an industrial facility and their respective degrees of flammability, which are determined by two principal factors: (i) the use or function of the space, and (ii) the type of material stored in storage areas [8].
Peruvian fire safety regulations are delineated in Standard A.130 of the Ministry of Housing, Construction, and Sanitation [9]. This regulation mandates the installation of fire water systems, including hoses and automatic sprinklers, in non-industrial buildings—such as multifamily housing, commercial facilities, healthcare buildings [10], and offices—based on their floor area and height. For industrial buildings, Standard A.060 [9] requires a fire risk assessment as a compulsory regulatory measure, considering the diverse range of industrial processes, products, and inputs with varying levels of combustibility [11].
In the context of calculating fire load density within industrial settings, Standard A.060 is supplemented by the Technical Prevention Standard (NTP) 831, as promulgated by the Spanish National Institute for Safety and Health at Work [12]. The operationalization of NTP 831 is achieved through the Technical Guide to the Regulation on Fire Safety in Industrial Establishments (RSCIEI), which is issued by the Spanish Ministry of Industry, Trade, and Tourism [13]. This guide delineates specific fire load density values and hazard coefficients applicable to diverse productive and support areas, as well as storage sections, contingent upon the type of materials stored.
The literature identifies four studies pertinent to fire risk assessment in industrial activities: the application of the analytic hierarchy process to fire risk evaluation in industry [14]; the utilization of the Gretener method for determining a fire safety coefficient [15]; the examination of fire safety requirements in the food industry [16]; and the implementation of fire protection systems in the plastics industry [17]. These studies do not assess the influence of the environment on the fire load.
In the industrial sector, the fire load, which significantly impacts the fire risk level of a facility, primarily originates from processing and storage areas [18]. Rice milling companies in the Lambayeque region contribute the largest portion of the national milled rice production, accounting for 38% of the national total [19]. The rice milling plants in Lambayeque contribute the majority of the national production of milled rice, representing 38% of the national total, and have 98 active milling plants [19, 20]. Within these companies, the distribution of areas and, consequently, the level of fire risk are comparable; thus, it is imperative to implement measures to protect operations from such risks [21].
The layout of rice plants in Lambayeque generally consists of a covered area and an open-air area. The covered area includes the processing environments such as: (i) pre-cleaning; (ii) destoning; (iii) second cleaning sifting; (iv) dehusking; (v) separation via Paddy table; (vi) polishing; (vii) brightening; (viii) separation; (ix) color sorting; and (x) bagging, as well as support environments such as: (xi) offices; (xii) laboratories, among others. Within the covered area there are also storage areas such as: (i) finished product warehouse; (ii) bran warehouse; (iii) empty sack warehouse; (iv) general warehouse, among others. The open-air area consists of the maneuvering yard and the natural drying area for paddy rice.
In this study, the objective was to analyze the impact of plant layout dimensions on fire load density in the rice milling industry of Lambayeque, Peru. The remainder of this article is structured as follows: Section 2 outlines the research methodology, Section 3 presents the findings, and Section 4 discusses the conclusions.
Fire load calculations were performed using wood-equivalent mass per unit area (kg/m²) as the reference standard [22]. The quantitative fire load (or fire load density) model expressed in Eq. (1) was applied, which estimates the rate of heat or thermal energy release per unit area that would result from a building fire [13, 23]. Specifically, Eq. (1) allows the calculation of the total fire load density of a building sector, Qs, in which productive, support, and storage activities coexist:
$Q_s=\frac{\left(Q_{a c}+Q_{a l}\right)}{\left(A_{a c}+A_{a l}\right)} \times R_a\left[\mathrm{Mcal} / \mathrm{m}^2\right]$ (1)
where,
Qac is the fire load per unit area in productive and support areas (hereinafter referred to as functional areas);
Qal is the fire load per unit area in storage areas;
Aac is the total area of functional areas;
Aal is the total area of storage areas;
Ra is a dimensionless hazard correction coefficient.
To calculate Qac, Eq. (2) was used [16]:
$Q_{a c}==\left(\frac{\Sigma_i\left(q_i s_i c_i\right)}{A_{a c}}\right) \times R_a$ (2)
where, q1 is the fire load density of each functional area (MJ/m² or Mcal/m²), si is the area of each functional space (m²), and ci is the dimensionless combustibility hazard coefficient of each functional area.
Similarly, to calculate Qal, Eq. (3) was applied [16]:
$Q_{a l}=\left(\frac{\Sigma_j\left(q_j h_j s_j c_j\right)}{A_{a l}}\right) \times R_a$ (3)
where, qj is the fire load of each storage area according to the type of stored material (MJ/m³ or Mcal/m³), cj is the dimensionless combustibility hazard coefficient of each stored material, hj is the stacking height of the stored materials, and sj is the stacking area of the stored materials.
The study began with the characterization of four rice milling plants (RMPs) located in the Lambayeque region, considering the size of all existing areas—both functional and storage—during the period 2021–2023. For this purpose, data corresponding to functional areas were collected, including fire load density (qi), area of each functional space (si), and the respective hazard coefficient (ci), as defined in Formula (2). Likewise, data for storage areas included stacking height (hj), stacking area (sj), fire load density (qj), and the hazard coefficient associated with each stored material (cj), in accordance with Eq. (3).
Based on these data, the heat load of each functional area (cf) was calculated as the product of fire load density, area, and hazard coefficient (qi × si × ci), as presented in Appendix 1. Similarly, the heat load of each storage area (ca) was determined as the product of fire load, hazard coefficient, stacking height, and stacking area (qj × cj × hj × sj), as shown in Appendix 2. Using the calculated values of cf and ca, the most significant functional and storage areas were identified based on their highest contributions.
By combining Eqs. (2) and (3) and considering all functional and storage areas, the total fire load of the building is obtained using Eq. (4):
$Q_e=\frac{\left(Q_{a c} * A_{a c}+Q_{a l} * A_{a l}\right)}{\left(A_{a c}+A_{a l}\right)}$ (4)
Subsequently, the total fire load density model Qe, given by Eq. (1), was applied using the area (m²) of the most influential functional and storage areas.
In this section, the characterization of the functional and storage areas is presented, along with the calculation method used and the results obtained. Moreover, for this study, information from four rice milling plants (IP1, IP2, IP3 and IP4) in the Lambayeque region was considered, to which the established calculation process for the total fire load was applied.
3.1 Characterization of functional and storage areas
The first task was to perform the calculations for the heat load values of the functional areas (Cf) of the four plants (IP1, IP2, IP3, and IP4), determining the average and then calculating the thermal load (See Appendix 1). Similarly, carry out the calculations for the load values of the warehouse areas (See Appendix 2). The analysis showed that, among the functional areas, the maneuvering yard is the most influential, accounting for 54% of the total heat load (Appendix 1). With respect to storage areas, three zones were identified as having the greatest contribution to the total heat load: the rice husk warehouse (53%), the finished product warehouse (18%), and the empty sack warehouse (11%). The remaining functional and storage areas exhibit lower levels of contribution to the total heat load and are therefore considered less significant. Consequently, these areas were not included in the scenario analysis. The most relevant areas, according to Appendix 1 and 2, were:
AC1: Maneuvering yard,
AL1: Rice husk warehouse,
AL2: Finished product warehouse, and
AL3: Empty sack warehouse.
Based on relevant area, four analytical scenarios were stablished (see Table 1).
Table 1. Scenarios considered for calculating the total fire load density (Qe)
|
Scenario |
Description |
|
1 |
Maneuvering yard size (AC1) and resulting total fire load density (Qe), assuming AC1 increases while AL1, AL2, and AL3 remain constant. |
|
2 |
Rice husk warehouse size (AL1) and resulting total fire load density (Qe), assuming AL1 increases while AC1, AL2, and AL3 remain constant. |
|
3 |
Finished product warehouse size (AL2) and resulting total fire load density (Qe) assuming AL2 increases while AC1, AL1, and AL3 remain constant. |
|
4 |
Empty sack warehouse size (AL3) and resulting total fire load density (Qe), assuming AL3 increases while AC1, AL1, and AL2 remain constant. |
3.2 Application of the total fire load calculation model for the rice milling industry
The second task was to generate reasonable data to allow building graphs to facilitate analysis. For each of the relevant areas AC1, AL1, AL2, and AL3, the associated size was considered and designated as R1 as the first data point to be graphed. Taking into account the reality of the rice milling industry in Lambayeque, possible maximum values (P10) were established according to that context. The intermediate values P1 to P9 were determined randomly. Using Eq. (4), the influence of the area (m²) of the following spaces—the maneuvering yard (AC1), rice husk warehouse (AL1), finished product warehouse (AL2), and empty sack warehouse (AL3)—on the total fire load density of the building (Qe) was plotted, considering the four scenarios defined in Table 1.
As an example, based on the first scenario, by applying Eq. (4) with the results from Tables 2 and 3, the total fire load (Qe) of the rice milling plant IP1 is obtained:
Qac = 138.08 Mcal/m2
Aac = 8057.64 m2
Qal = 1497.82 Mcal/m2
Aal = 7455.21 m2
Qe = 791.55 Mcal/m2
AC1 = 6,424 m2 (R1) is used along with its respective fire load Qe = 791.55; then, 10 increasing random data points (P1 to P10) are generated within a range from 6,424 m2 up to 12,000 m2, which is approximately twice the actual size in this case. From the knowledge available about the rice milling industry in Lambayeque, the largest maneuvering yard is less than 15,000 m2.
Table 2. Areas of functional environments and resulting fire load (Qac), case IP1
|
Area |
q1 |
c |
Ra |
Aac 2 |
q*s*c* |
|
Process |
144 |
1 |
1.5 |
528.66 |
76127 |
|
Bagging |
96 |
2 |
1 |
183.21 |
28141 |
|
Homogenized |
144 |
1 |
1.5 |
398.57 |
57394 |
|
By-products bagging |
144 |
2 |
1.5 |
25 |
5760 |
|
Workshops |
119 |
2 |
1.5 |
51.9 |
9881 |
|
Lab and QC3 |
48 |
1 |
1 |
35 |
2184 |
|
Administrative4 |
144 |
1 |
1 |
329.78 |
61734 |
|
Dining5 and kitchen |
72 |
1 |
1 |
51.53 |
4823 |
|
Maneuvering yard |
48 |
2 |
1 |
6424 |
493362 |
|
Work crew5 |
48 |
2 |
1 |
30 |
2304 |
|
Total |
8057.6 |
741712 |
|||
|
max Ra |
1.5 |
||||
|
Qac (Formula 2) |
138 |
||||
Table 3. Storage areas and resulting quantity (Qal), case IP1
|
Area |
q1 |
c |
Ra |
Aal2 |
h3 |
q*c*h*s |
|
Rice husk4 |
192 |
1 |
1.5 |
4037.4 |
3.5 |
3527029 |
|
Finished product4 |
192 |
1 |
1.5 |
926.25 |
3.6 |
832291 |
|
Rice dust4 |
192 |
1 |
1.5 |
361.28 |
3.5 |
315614 |
|
Wooden pallets |
313 |
1 |
2 |
594 |
0.14 |
26029 |
|
Empty sack4 |
6058 |
1 |
2 |
1287.5 |
0.01 |
101398 |
|
Harvest sack4 |
6058 |
1 |
2 |
245 |
0.4 |
771789 |
|
Transport4 |
601 |
1 |
2 |
1.3 |
0.3 |
304 |
|
Workshop5 |
601 |
2 |
2 |
1.5 |
0.3 |
432 |
|
LPG6 |
10505 |
2 |
2 |
1 |
0.5 |
8404 |
|
Total |
7455.2 |
5583292 |
||||
|
max Ra |
2 |
|||||
|
Qal (Formula 3) |
1497 |
|||||
Table 4. Rate of change of AC1 and Qe for IP1 in Scenario 1
|
AC11 (m2) |
Qe (Mcal/m2) |
AC1 Growth Rate |
Qe Growth Rate |
|
|
R1 |
6424 |
791.55 |
- |
- |
|
P1 |
6582 |
784.73 |
2% |
-1% |
|
P2 |
6890 |
771.82 |
5% |
-2% |
|
P3 |
7750 |
738.29 |
12% |
-4% |
|
P4 |
8286 |
719.07 |
7% |
-3% |
|
P5 |
9198 |
688.95 |
11% |
-4% |
|
P6 |
9199 |
688.92 |
0% |
0% |
|
P7 |
9356 |
684.03 |
2% |
-1% |
|
P8 |
9538 |
678.48 |
2% |
-1% |
|
P9 |
10655 |
646.61 |
12% |
-5% |
|
P10 |
11812 |
617.19 |
11% |
-5% |
|
Average growth rate |
6% |
-2% |
||
Subsequently, the average rate of variation for the generated value pairs (P1 to P10) was calculated; this allowed for the determination of the relationship between both variables. Continuing with the previous example, for IP1, an average variation in the maneuvering yard size of 6% results in a decrease of -2% in the total fire load, which means that the larger the maneuvering yard, the lower the fire risk in the rice plant (see Table 4). Similarly, the calculation was performed with the maneuvering yard areas and fire load of the other rice plants (IP2, IP3, and IP4), in the other three scenarios (see Appendix 3). The results for each of the four scenarios are presented in Figures 1-4, respectively.
In Scenario 1, an inverse relationship is observed between AC1 and Qe; as AC1 increases, Qe decreases. This behavior is explained by the relatively low fire load density (q) of AC1 [11], since this area is open and uncovered, and by its low hazard correction coefficient (Ra). This trend is illustrated in Figure 1. In contrast, in Scenarios 2, 3, and 4, increasing the areas of AL1, AL2, and AL3 leads to an increase in Qe. This behavior is attributed to the high fire load densities (q) associated with these storage areas compared to AC1 [11], as shown in Figures 2-4.
Figure 1. Scenario 1: Total fire load density as a function of the maneuvering yard area
Figure 2. Scenario 2: Total fire load density as a function of the rice husk warehouse area
Figure 3. Scenario 3: Total fire load density as a function of the finished product warehouse area
Figure 4. Scenario 4: Total fire load density as a function of the empty sack warehouse area
In analyzing the four scenarios, the simulation reveals that an increase in the maneuvering yard's size (AC1) is associated with a reduction in the total fire load (Qe), as depicted in Figure 1. In contrast, for the remaining three scenarios, an enlargement of the warehouse area is associated with a rise in the total fire load (Qe). This pattern is due to the maneuvering yard's relatively low unit fire load (qi) of 48 Mcal/m2 and a hazard coefficient of Ra=1. Conversely, the unit fire load (qj) in the warehouses is considerably higher, measuring 192 Mcal/m3 for both the rice husk and finished product warehouse, and 6058 Mcal/m3 for the empty sack warehouse. Additionally, the dimensionless hazard coefficient for these warehouses ranges between 1.5 and 2.
On the other hand, few studies have been found in the literature related to fire load in the rice milling industry. In particular, a study by Willis and Llaja [11] was found for the metal-mechanical industry, which analyzes the fire risk for a facility, determining a total fire load of 140.08 Mcal/m². This fire load is lower than in the case of the rice milling industry, which averages 800 Mcal/m² in Lambayeque. This highlights that the rice milling industry presents higher fire-related risks. Despite this, an evaluation in the study by Hahnemann et al. [15] concludes that, in the facilities of said metal-mechanical industry, fire protection is insufficient. This makes it clear that, in addition to design, operation also presents factors that can cause a fire.
Also, Tascón [16] examines fire safety requirements within the agri-food sector, concluding that the dimensions of warehouses and the selected fire compartmentalization are of significant importance; similar to the arguments presented in the conclusions of this study. Additionally, Andrade [3] emphasizes that the appropriate selection of a building's shape and configuration is a crucial factor in controlling fire spread. These findings substantiate the analysis of dimensions conducted in this study concerning the rice milling industry [24].
The practical implication of this article is the recommendation to examine the design from the project's inception, particularly in industrial applications, such as the rice milling industry, as similarly discussed in the study by Seong and Leman [25].
This study examines the impact of spatial dimensions in both functional and storage areas on the overall fire load density in rice milling industry facilities situated in Lambayeque. A detailed assessment of the spatial configurations of four rice milling plants in the region was undertaken. The findings indicate that the maneuvering yard is a significant factor in determining the heat load of functional areas, accounting for 54% of the total heat load. Regarding storage areas, the paddy rice warehouse (53%), the finished product warehouse (18%), and the empty sacks warehouse (11%) were identified as the primary contributors to the total heat load.
The total fire load calculation model was applied to the four evaluated plants, facilitating an analysis of the impact of the spatial dimensions. This analysis involved four scenarios, structured in pairs, which considered both the dimensions of each space and the building's overall thermal fire load.
The analysis reveals that expanding the maneuvering yard leads to a reduction in the building's overall thermal load (Qe). This reduction is attributed to the environment's low fire load density and a decreased hazard coefficient (Ra). Conversely, in the other three scenarios, it was found that increasing the areas designated for paddy rice storage, finished products, and empty sacks results in a rise in Qe. This rise is due to the higher fire load densities and Ra coefficient values in these areas, as opposed to those in the maneuvering yard.
From a quantitative standpoint, it has been estimated that (i) expanding the maneuvering yard by 4% to 10% results in a reduction of the fire load by 2% to 3%, and (ii) increasing the storage of paddy rice (by 10% to 21%), finished products (by 13% to 19%), and empty sacks (by 14% to 22%) results in corresponding increases in fire load by 3% to 8%, 2% to 4%, and 1% to 3%, respectively.
Through scenario analysis, the relationship between the size of the selected areas and the total thermal load of the building was evaluated. An inverse relationship was observed.
An inverse relationship was observed in the maneuvering yard, attributable to its low fuel load. Conversely, direct relationships were identified in the three storage facilities.
The findings suggest that, from the outset of architectural design, it is crucial to accurately estimate the dimensions of spaces within industrial facilities for rice milling, particularly those areas that significantly influence fire load, such as the maneuvering yard and the primary warehouses. The spatial design of the plant should aim to minimize the building's total weighted fire load, thereby reducing fire risk, enhancing safety, and decreasing the costs associated with the implementation of fire protection systems, as well as the likelihood of human casualties in the event of an emergency.
Appendix 1. Area and thermal load of functional areas
|
Functional Area |
Parameter |
Rice Milling Plants (IP) |
|
Thermal Load (%) |
|||
|
IP1 |
IP2 |
IP3 |
IP4 |
||||
|
Processing |
q (Mcal/m2) |
144 |
144 |
144 |
144 |
|
|
|
C |
1 |
1 |
1 |
1 |
|
|
|
|
Ra |
1.5 |
1.5 |
1.5 |
1.5 |
|
|
|
|
s (m2) |
528.66 |
6288.68 |
519.56 |
772.86 |
|
|
|
|
Cf = q*s*c |
76127.04 |
905569.92 |
74816.64 |
111291.84 |
291951.36 |
28% |
|
|
Bagging |
q (Mcal/m2) |
96 |
96 |
|
96 |
|
|
|
C |
1.6 |
1.6 |
|
1.6 |
|
|
|
|
Ra |
1 |
1 |
|
1 |
|
|
|
|
s (m2) |
183.21 |
79.37 |
|
173.71 |
|
|
|
|
Cf = q*s*c |
28141.06 |
12191.23 |
|
26681.86 |
22338.05 |
2% |
|
|
Homogenization |
q (Mcal/m2) |
144 |
- |
144 |
144 |
|
|
|
C |
1 |
- |
1 |
1 |
|
|
|
|
Ra |
1.5 |
- |
1.5 |
1.5 |
|
|
|
|
s (m2) |
398.57 |
- |
301.7 |
378.57 |
|
|
|
|
Cf = q*s*c |
57394.08 |
- |
43444.80 |
54514.08 |
51784.32 |
5% |
|
|
By-product Bagging |
q (Mcal/m2) |
144 |
- |
- |
- |
|
|
|
C |
1.6 |
- |
- |
- |
|
|
|
|
Ra |
1.5 |
- |
- |
- |
|
|
|
|
s (m2) |
25 |
- |
- |
- |
|
|
|
|
Cf = q*s*c |
5760.00 |
- |
- |
- |
5760.00 |
1% |
|
|
Workshops |
q (Mcal/m2) |
119 |
119 |
119 |
119 |
|
|
|
C |
1.6 |
1.6 |
1.6 |
1.6 |
|
|
|
|
Ra |
1.5 |
1.5 |
1.5 |
1.5 |
|
|
|
|
s (m2) |
51.9 |
90.48 |
151.66 |
71.9 |
|
|
|
|
Cf = q*s*c |
9881.76 |
17227.39 |
28876.06 |
13689.76 |
17418.74 |
2% |
|
|
Laboratories and Quality Control |
q (Mcal/m2) |
48 |
- |
48 |
48 |
|
|
|
C |
1.3 |
- |
1.3 |
1.3 |
|
|
|
|
Ra |
1 |
- |
1 |
1 |
|
|
|
|
s (m2) |
35 |
- |
30.08 |
47 |
|
|
|
|
Cf = q*s*c |
2184.00 |
- |
1876.99 |
2932.80 |
2331.26 |
0% |
|
|
Administrative Offices |
q (Mcal/m2) |
144 |
144 |
144 |
144 |
|
|
|
C |
1.3 |
1.3 |
1.3 |
1.3 |
|
|
|
|
Ra |
1 |
1 |
1 |
1 |
|
|
|
|
s (m2) |
329.78 |
283.19 |
75.36 |
270.78 |
|
|
|
|
Cf = q*s*c |
61734.82 |
53013.17 |
14107.39 |
50690.02 |
44886.35 |
4% |
|
|
Dining Area + Kitchen |
q (Mcal/m2) |
72 |
72 |
72 |
72 |
|
|
|
C |
1.3 |
1.3 |
1.3 |
1.3 |
|
|
|
|
Ra |
1 |
1 |
1 |
1 |
|
|
|
|
s (m2) |
51.53 |
188.91 |
59.07 |
71.93 |
|
|
|
|
Cf = q*s*c |
4823.21 |
17681.98 |
5528.95 |
6732.65 |
8691.70 |
1% |
|
|
Maneuvering Yard (AC1) |
q (Mcal/m2) |
48 |
48 |
48 |
48 |
|
|
|
C |
1.6 |
1.6 |
1.6 |
1.6 |
|
|
|
|
Ra |
1 |
1 |
1 |
1 |
|
|
|
|
s (m2) |
6423.99 |
7459.33 |
11044.67 |
4073.89 |
7250.47 |
|
|
|
Cf = q*s*c |
493362.43 |
572876.54 |
848230.66 |
312874.75 |
556836.10 |
54% |
|
|
Work Crews Area |
q (Mcal/m2) |
48 |
48 |
- |
48 |
|
|
|
C |
1.6 |
1.6 |
- |
1.6 |
|
|
|
|
Ra |
1 |
1 |
- |
1 |
|
|
|
|
s (m2) |
30 |
1260.17 |
- |
41 |
|
|
|
|
Cf = q*s*c |
2304.00 |
96781.06 |
- |
3148.80 |
34077.95 |
3% |
|
|
Electrical Substation |
q (Mcal/m2) |
- |
72 |
72 |
72 |
|
|
|
C |
- |
1.3 |
1.3 |
1.3 |
|
|
|
|
Ra |
- |
1.5 |
1.5 |
1.5 |
|
|
|
|
s (m2) |
- |
31.45 |
34.76 |
27.8 |
|
|
|
|
Cf = q*s*c |
- |
2943.72 |
3253.54 |
2602.08 |
2933.11 |
0% |
|
|
|
|
|
|
|
Total |
1039008.94 |
100% |
Appendix 2. Area and thermal load of storage areas
|
Storage Area |
Parameters |
Rice Milling Plants (IP) |
|
Thermal Load of the Area (%) |
|||
|
IP1 |
IP2 |
IP3 |
IP4 |
||||
|
Rice husk warehouse (AL1) |
q (Mcal/m3) |
192 |
192 |
192 |
192 |
|
|
|
C |
1.3 |
1.3 |
1.3 |
1.3 |
|
|
|
|
Ra |
1.5 |
1.5 |
1.5 |
1.5 |
|
|
|
|
s (m2) |
4037.35 |
757.83 |
2877.29 |
3877.35 |
2887.46 |
|
|
|
Height (m.) |
3.5 |
8 |
3.5 |
3.5 |
|
|
|
|
Ca = q*c*h*s |
3527028.96 |
1513234.94 |
2513600.54 |
3387252.96 |
2735279.35 |
53% |
|
|
Finished product warehouse (AL2) |
q (Mcal/ m3) |
192 |
192 |
192 |
192 |
|
|
|
C |
1.3 |
1.3 |
1.3 |
1.3 |
|
|
|
|
Ra |
1.5 |
1.5 |
1.5 |
1.5 |
|
|
|
|
s (m2) |
926.25 |
1090.58 |
1183.29 |
786.25 |
996.59 |
|
|
|
Height (m.) |
3.6 |
4.2 |
3.6 |
3.6 |
|
|
|
|
Ca = q*c*h*s |
832291.20 |
1143276.83 |
1063257.06 |
706492.80 |
936329.47 |
18% |
|
|
Straw bale storage area |
q (Mcal/m3) |
- |
192 |
192 |
- |
|
|
|
C |
- |
1.3 |
1.3 |
- |
|
|
|
|
Ra |
- |
1.5 |
1.5 |
- |
|
|
|
|
s (m2) |
- |
181.25 |
597.93 |
- |
|
|
|
|
Height (m.) |
- |
4 |
2.4 |
- |
|
|
|
|
Ca = q*c*h*s |
- |
180960.00 |
358183.99 |
- |
269571.99 |
5% |
|
|
Rice dust storage area |
q (Mcal/m3) |
300 |
300 |
- |
300 |
|
|
|
C |
1.3 |
1 |
- |
1.3 |
|
|
|
|
Ra |
1.5 |
1 |
- |
1.5 |
|
|
|
|
s (m2) |
361.28 |
106.92 |
- |
381.78 |
|
|
|
|
Height (m.) |
3.5 |
4.2 |
- |
3.5 |
|
|
|
|
Ca = q*c*h*s |
493147.20 |
134719.20 |
- |
521129.70 |
382998.70 |
7% |
|
|
Wooden pallets |
q (Mcal/m3) |
313 |
313 |
- |
313 |
|
|
|
C |
1 |
1 |
- |
1 |
|
|
|
|
Ra |
2 |
2 |
- |
2 |
|
|
|
|
s (m2) |
594 |
1306.8 |
- |
484 |
|
|
|
|
Height (m.) |
0.14 |
0.14 |
- |
0.14 |
|
|
|
|
Ca = q*c*h*s |
26029.08 |
57263.98 |
- |
21208.88 |
34833.98 |
1% |
|
|
Empty sack warehouse (AL3) |
q (Mcal/m3) |
6058 |
6058 |
6058 |
6058 |
|
|
|
C |
1.3 |
1.3 |
1.3 |
1.3 |
|
|
|
|
Ra |
2 |
2 |
2 |
2 |
|
|
|
|
s (m2) |
245 |
152 |
194 |
275 |
216.50 |
|
|
|
Height (m.) |
0.4 |
0.1 |
0.3 |
0.4 |
|
|
|
|
Ca = q*c*h*s |
771789.20 |
119706.08 |
458348.28 |
866294.00 |
554034.39 |
11% |
|
|
Enamel paint, thinner, etc. |
q (Mcal/m3) |
6058 |
601 |
601 |
601 |
|
|
|
C |
1.3 |
1.6 |
1.3 |
1.3 |
|
|
|
|
Ra |
2 |
2 |
2 |
2 |
|
|
|
|
s (m2) |
245 |
0.1 |
6.8 |
1.3 |
|
|
|
|
Height (m.) |
0.4 |
0.3 |
0.3 |
0.3 |
|
|
|
|
Ca = q*c*h*s |
771789.20 |
28.85 |
1593.85 |
304.71 |
193429.15 |
4% |
|
|
Liquefied Petroleum Gas (LPG) |
q (Mcal/m3) |
10505 |
10505 |
- |
10505 |
|
|
|
C |
1.6 |
1.6 |
- |
1.6 |
|
|
|
|
Ra |
2 |
2 |
- |
2 |
|
|
|
|
s (m2) |
1 |
1 |
- |
5 |
|
|
|
|
Height (m.) |
0.5 |
0.5 |
- |
0.5 |
|
|
|
|
Ca = q*c*h*s |
8404.00 |
8404.00 |
- |
42020.00 |
19609.33 |
0% |
|
|
|
|
|
|
|
Total |
5126086.37 |
100% |
Appendix 3. Scenarios for calculating the total fire load
Scenario 1. Maneuvering yard size (AC1) and resulting fire load (Qe), with AC1 increasing, AL1, AL2 and AL3 constant
|
Area and Fire Load |
Rice Milling Plants (IP) |
|||||||
|
IP1 |
IP2 |
IP3 |
IP4 |
|||||
|
AC1 |
Qe |
AC1 |
Qe |
AC1 |
Qe |
AC1 |
Qe |
|
|
R1 |
6424 |
791.55 |
7459 |
440.12 |
11045 |
603.74 |
4074 |
911.96 |
|
P1 |
6582 |
784.73 |
8330 |
428.69 |
11362 |
595.55 |
4892 |
864.49 |
|
P2 |
6890 |
771.82 |
8525 |
426.24 |
12568 |
566.80 |
5989 |
809.06 |
|
P3 |
7750 |
738.29 |
8640 |
424.82 |
12685 |
564.20 |
6096 |
804.09 |
|
P4 |
8286 |
719.07 |
8761 |
423.33 |
13662 |
543.55 |
7441 |
747.18 |
|
P5 |
9198 |
688.95 |
9362 |
416.15 |
14088 |
535.13 |
7973 |
727.18 |
|
P6 |
9199 |
688.92 |
9459 |
415.03 |
14089 |
535.11 |
8086 |
723.10 |
|
P7 |
9356 |
684.03 |
10729 |
401.01 |
15361 |
511.83 |
8653 |
703.39 |
|
P8 |
9538 |
678.48 |
11633 |
391.81 |
16423 |
494.28 |
9202 |
685.49 |
|
P9 |
10655 |
646.61 |
12925 |
379.64 |
16624 |
491.13 |
9739 |
669.01 |
|
P10 |
11812 |
617.19 |
14937 |
362.68 |
16667 |
490.47 |
10188 |
655.94 |
Scenario 2. Rice husk warehouse size (AL1) and resulting fire load (Qe), with AL1 increasing, AC1, AL2 and AL3 constant
|
Area and Fire Load |
Rice Milling Plants (IP) |
|||||||
|
IP1 |
IP2 |
IP3 |
IP4 |
|||||
|
AL1 |
Qe |
AL1 |
Qe |
AL1 |
Qe |
AL1 |
Qe |
|
|
R1 |
4037.35 |
791.55 |
757.83 |
440.12 |
2877.29 |
603.74 |
3877.35 |
911.96 |
|
P1 |
4157 |
798.86 |
761 |
440.59 |
3163 |
621.01 |
4500 |
950.38 |
|
P2 |
4483 |
818.23 |
909 |
462.45 |
4928 |
717.14 |
4743 |
964.43 |
|
P3 |
5961 |
896.98 |
1176 |
501.23 |
5172 |
729.15 |
5009 |
979.26 |
|
P4 |
6489 |
921.97 |
1663 |
569.80 |
7144 |
816.85 |
6549 |
1055.14 |
|
P5 |
6937 |
942.05 |
1970 |
611.66 |
7205 |
819.32 |
6659 |
1059.99 |
|
P6 |
7154 |
951.42 |
2927 |
735.82 |
7577 |
834.12 |
6826 |
1067.23 |
|
P7 |
7967 |
984.70 |
3253 |
776.06 |
7950 |
848.49 |
8125 |
1118.70 |
|
P8 |
9745 |
1048.59 |
4302 |
899.06 |
8134 |
855.41 |
8751 |
1140.81 |
|
P9 |
10140 |
1061.35 |
4577 |
929.76 |
8429 |
866.29 |
9002 |
1149.25 |
|
P10 |
10416 |
1070.00 |
4627 |
935.28 |
8770 |
878.54 |
9925 |
1178.36 |
Scenario 3. Finished product warehouse size (AL2) and resulting fire load (Qe), with AL2 increasing, AC1, AL1 and AL3 constant
|
Area and Fire Load |
Rice Milling Plants (IP) |
|||||||
|
IP1 |
IP2 |
IP3 |
IP4 |
|||||
|
AL2 |
Qe |
AL2 |
Qe |
AL2 |
Qe |
AL2 |
Qe |
|
|
R1 |
926.25 |
791.55 |
1090.58 |
440.12 |
1183.29 |
603.74 |
786.25 |
911.96 |
|
P1 |
972 |
794.50 |
1128 |
442.71 |
1630 |
631.69 |
893 |
919.22 |
|
P2 |
1237 |
811.30 |
2071 |
505.40 |
2372 |
675.33 |
1369 |
950.18 |
|
P3 |
1512 |
828.14 |
2567 |
536.50 |
2639 |
690.24 |
1994 |
987.67 |
|
P4 |
1895 |
850.65 |
2735 |
546.76 |
2732 |
695.34 |
2249 |
1002.02 |
|
P5 |
1937 |
853.06 |
3067 |
566.65 |
2978 |
708.61 |
3028 |
1042.89 |
|
P6 |
2217 |
868.79 |
3571 |
595.89 |
3152 |
717.81 |
3150 |
1048.92 |
|
P7 |
2460 |
882.02 |
3667 |
601.33 |
3256 |
723.23 |
3284 |
1055.42 |
|
P8 |
2677 |
893.53 |
3698 |
603.08 |
3562 |
738.88 |
3361 |
1059.11 |
|
P9 |
3369 |
928.35 |
3817 |
609.75 |
3647 |
743.14 |
3503 |
1065.81 |
|
P10 |
3618 |
940.23 |
3941 |
616.65 |
3814 |
751.42 |
3924 |
1084.99 |
Scenario 4. Empty sack warehouse size (AL3) and resulting fire load (Qe), with AL3 increasing, AC1, AL1 and AL2 constant
|
Area and Fire Load |
Rice Milling Plants (IP) |
|||||||
|
IP1 |
IP2 |
IP3 |
IP4 |
|||||
|
AL3 |
Qe |
AL3 |
Qe |
AL3 |
Qe |
AL3 |
Qe |
|
|
R1 |
245.00 |
791.55 |
152.00 |
440.12 |
194.00 |
603.74 |
275.00 |
911.96 |
|
P1 |
268 |
799.70 |
288 |
446.54 |
279 |
622.46 |
280 |
914.05 |
|
P2 |
392 |
843.26 |
315 |
447.80 |
470 |
663.91 |
372 |
952.14 |
|
P3 |
444 |
861.32 |
373 |
450.52 |
552 |
681.46 |
385 |
957.47 |
|
P4 |
574 |
905.95 |
405 |
452.01 |
637 |
699.48 |
660 |
1067.96 |
|
P5 |
625 |
923.26 |
458 |
454.46 |
683 |
709.17 |
720 |
1091.46 |
|
P6 |
668 |
937.77 |
519 |
457.28 |
737 |
720.48 |
748 |
1102.35 |
|
P7 |
676 |
940.46 |
602 |
461.09 |
762 |
725.70 |
796 |
1120.93 |
|
P8 |
722 |
955.88 |
637 |
462.69 |
792 |
731.94 |
930 |
1172.08 |
|
P9 |
868 |
1004.24 |
745 |
467.60 |
854 |
744.78 |
944 |
1177.36 |
|
P10 |
887 |
1010.47 |
971 |
477.72 |
971 |
768.78 |
987 |
1193.53 |
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(Average)
(Average)