1. Introduction
The energy transition lies at the core of international
climate commitments, with approximately one quarter of global carbon dioxide
emissions stemming from industrial energy consumption (Ghionda, 2024).
This reality compels various industrial sectors to enhance their energy
efficiency. Among these, the health sector, including pharmaceutical
manufacturing, stands out due to its significant carbon footprint, accounting for
an average of 4.9% of global emissions (Keil, 2024; Chen-Xu, 2024; Harm, 2019).
The pharmaceutical sector is one of the most energy-intensive within the
chemical industry, consuming up to 50% of the energy used in production
facilities alone. Moreover, its greenhouse gas emissions exceed those of the
automotive sector by 55% (Belkhir & Elmeligi, 2019), and its carbon
footprint is increasing at a faster rate than both the healthcare services
sector and the recent global average (Rosalie et al., 2025), despite receiving
comparatively less attention (LEEM, 2023). Due to the complexity, costliness,
and time-consuming nature of its processes, as well as its substantial energy
consumption (Alzhrani, 2024), the pharmaceutical industry faces increasingly
stringent economic, environmental, and regulatory challenges. Rising energy
costs, dependence on fossil fuels, and mounting pressure from greenhouse gas
(GHG) reduction policies make energy management a top strategic priority.
In the
Middle East and North Africa (MENA) region, healthcare systems are challenged
by a rising prevalence of non-communicable diseases, economic constraints, and
fragmented health systems. In 2018, healthcare expenditure ranged from 2.49% to
8.35% of GDP depending on the country, with pharmaceutical expenditure
averaging 24.8% of total healthcare spending—significantly higher than the OECD
average of 17.4% (Kamphuis, 2021). This imbalance, combined with substantial
out-of-pocket payments by patients, ranging from 6% in Oman to 62% in Egypt of
current healthcare expenditure (WHO, 2018), necessitates urgent cost rationalization
policies, particularly focused on controlling pharmaceutical costs given their
large contribution to overall health expenditure (Kanavos, 2020).
Within this context, optimizing
pharmaceutical industrial processes, especially energy-intensive ones such as
drying, represents a strategic opportunity to enhance the sector’s economic and
environmental sustainability. This pressure is particularly acute in industrial
pharmaceutical establishments (IPEs) in the Arab world, where production is
primarily focused on generic drugs with lower profit margins than branded drugs
(Nguyen, 2022; Frank, 2021). The generic pharmaceutical market in the region is
projected to grow by 40% in volume and 12% in value (IQVIA, 2022).
Simultaneously, 95% of the region’s energy demand is met by fossil fuels, one
of the highest shares worldwide according to the International Energy Agency
(IEA), with annual per capita CO2 emissions of 13 tons, lower than North
America (17 tons) but higher than Europe (7.8 tons) (Mohtadi, 2025).
In this
scenario, improving energy performance becomes critical not only for reducing
production costs but also for advancing sustainable development goals.
Consequently, Arab countries such as Saudi Arabia, Bahrain, and the United Arab
Emirates have set ambitious net-zero emission targets (Elfarra, 2024). However,
the lack of technical data and targeted initiatives addressing energy-intensive
processes, such as drying, constitutes a major obstacle to improving energy
efficiency in the pharmaceutical sector within Arab countries, despite their
strong stake in sustainable transition objectives.
While global progress has been made in
pharmaceutical energy optimization, very few studies have addressed this issue
within the Arab region. In particular, rigorous model-based approaches to
optimize energy-intensive processes like drying remain scarce. Industrial
practices tend to remain empirical and lack integration of simulation tools or
digital frameworks that could drive performance improvements. From a
theoretical perspective, the absence of region-specific frameworks limits the
applicability of international solutions.
This study aims to fill both theoretical
and practical gaps by evaluating the performance of drying systems in a
Moroccan pharmaceutical plant. It integrates real-world data collection,
simulation modeling, and energy-performance analysis to propose optimized
parameters for fluidized bed drying. To our knowledge, this is the first
comprehensive model-based evaluation of pharmaceutical drying energy in the
MENA region, providing both a scalable methodology and practical
recommendations for local adoption.
To address these challenges, the study
proposes an integrated approach to energy optimization in pharmaceutical
drying, combining industrial data from a Moroccan site with a robust physical
simulation model. The objective is to identify key operational parameters
affecting energy consumption and to recommend optimized conditions that
maintain product quality while reducing energy usage and emissions.
This work contributes to the literature in
three main ways: Offering a rare data-driven analysis of pharmaceutical
manufacturing energy use in North Africa, a region where such studies are
lacking, applying a two-phase fluidized bed drying model under real industrial
constraints, providing a replicable methodology; delivering concrete
recommendations to support the adoption of structured energy management systems
in pharmaceutical plants across the Arab region.
The article is structured as follows: The
first section reviews the literature on energy management and optimization in
pharmaceutical production, focusing on the MENA region. The second part
presents a case study on drying process optimization through modeling and
simulation. The final section draws lessons from the results and outlines
strategic recommendations for sustainable energy practices in the
pharmaceutical sector.
2.
Literature Review
2.1.
The Pharmaceutical Industry and Energy Challenges
The pharmaceutical
industry extensively relies on steam and electricity to meet its specific
operational needs, with approximately 50% of this energy associated with
manufacturing processes (Chaitanya Sampat, 2022). In Austria, the
pharmaceutical sector accounts for about 1.5% of the national natural gas
consumption, primarily used for process steam generation via gas-fired boilers (Beck,
2024). The pharmaceutical and chemical industries rank among the most
energy-intensive sectors in several countries, notably France and Germany,
where they represent 32% of industrial energy consumption (Geres, et al., 2019;
Chareyron, Moliranaro, & Multon, 2021). The share of renewable energy
within this consumption remains largely undocumented, particularly in the Arab
world.
However, it is well
established that this region is highly dependent on fossil fuels, which still
constitute 95% of its energy sources, the highest share worldwide according to
the International Energy Agency (IEA, 2021) (Figure 1). Various programs
aim to reduce this dependence. Indeed, non-oil-producing countries such as
Morocco and Egypt have integrated 25 % and 17.1 % renewable energy into their
energy mix, whereas oil-producing nations like Saudi Arabia and the United Arab
Emirates have renewable shares below 1% (IEA, 2021; Abed, 2025; El Hafdaoui,
2025).
Although fossil fuel
resources have supported the economic development of many of these countries,
they face challenges such as reserve depletion, price volatility, and the
impacts of climate change (ESCWA, 2023).
Fig. 1: Total final energy consumption breakdown by source in the Arab region,
2020 (IEA, 2021)
In the pharmaceutical industry, energy efficiency has
often been relegated to a secondary priority compared to other high-energy-consuming
industries. However, regulatory pressure, global climate targets, and the
imperative to reduce CO₂ emissions at the process level are now driving a
strategic shift towards energy sustainability (Beck, 2024). According to a
recent study (Rosalie, et al., 2025), greenhouse gas emissions increased by 77%
between 1995 and 2019, mainly driven by the United States and China, and linked
to the rising consumption of pharmaceuticals as well as supply chain
modalities. Technological factors, however, contributed significantly to
emission reductions, mitigating growth by approximately 107%.
Regarding
Scope 1 and 2 emissions, often underreported in corporate disclosures, they
range from 31 to 326 tonnes of CO₂ per million dollars of pharmaceuticals
produced (Rosalie, et al., 2025; Belkhir & Elmeligi, 2019). For example, in
Germany, the pharmaceutical and chemical industries are responsible for
one-third of total greenhouse gas emissions (Geres, et al., 2019). When
considering the pharmaceutical life cycle, the bulk manufacturing, research and
development (R&D), and formulation/packaging phases are the most
energy-intensive. Thus, the energy challenges faced by the pharmaceutical
sector, particularly in the Arab world, are both economic and environmental. To
address these challenges, a non-exhaustive list of potential actions is
presented in (Figure 2) (Capgemini, 2023).
Various initiatives
contribute to the ongoing energy transition within the pharmaceutical sector.
Although their implementation remains limited in the Arab region, several
emerging efforts driven by industrial stakeholders and sectoral policies
demonstrate notable progress. These actions can be categorized according to
three fundamental levers: reduction, reuse, and replacement (see the table
above for a non-exhaustive summary of documented cases).
The specific case of
fluidized bed drying, a major energy-consuming operation, is addressed in
detail in the following section. Among the identified strategies, optimization
of heating, ventilation, and air conditioning (HVAC) systems stands out as the
most studied and widely implemented, particularly in developed countries.
HVAC systems account for
primary energy consumption in pharmaceutical production facilities (Figure 3)
due to stringent environmental control requirements (temperature, humidity,
pressure, air quality), especially in classified cleanroom areas (Capgemini, 2023).
Increasingly, energy
consumption optimization relies on approaches combining numerical modeling and
artificial intelligence, enabling reductions in energy use ranging from 8% to
35% (Dhage, 2016; M’baye, 2022; Gupta, 2023; Ozelame, 2024; Liu, 2024;
Alassafi, 2024).
Thanks to an innovative design of the
lyophilization system, a study (Keller, 2025) demonstrates a potential reduction in electric
heating demand of up to 25% for large-scale installations, while improving
synergy between the cooling and drying phases.
Fig. 2: Possible courses of action to improve energy performance in
the pharmaceutical industry (Capgemini, 2023)
|
|
|
HVAC: Ventilation for clean rooms and fume hoods; area
requiring 100% make-up air; chilled water, Hot water, and stream, particle
control ventilation…
Plug load and processes: Centrifuges, sterilization process, dryers,
mixer, motor, incubators, analysis equipment…
|
Fig. 3: Energy consumption
split of a US Pharma company by process and nature (Capgemini, 2023)
Within the broader
context of dry dosage form production, another study demonstrated energy
savings of up to 30% by transitioning from batch production to continuous
manufacturing (PAT) (Sampat, 2022). Additionally, a separate study reported
energy savings of 83.3% (Chen, 2023).
Beyond thermal processes
such as drying, industrial utilities like compressed air represent a
significant energy consumer in pharmaceutical manufacturing. A study conducted
in packaging demonstrated that optimization at two levels, production (source
point) and usage (user point), enabled a 23% reduction in compressor
electricity consumption and a 7 to 8% decrease in compressed air usage (Candra,
2024). These findings highlight the potential for energy savings even in
peripheral equipment, which is often overlooked, aligning closely with the
objectives of this study: to identify and optimize energy performance levers
within processes, particularly drying, through a combined approach of field
observation and technical modeling.
Furthermore, another
study showed that the intelligent integration of low-carbon hybrid
technologies, enabled by customized energy modeling and multi-objective
optimization, including renewable energies, heat recovery, and process
electrification, resulted in significant emission reductions at moderate costs
(Ghiondaun, 2024). Although several initiatives have been documented (Table 1),
data within Arab countries remain scarce and often lack conclusive evidence.
Beyond actions targeting
energy consumption, additional improvement levers can be identified,
particularly the eco-design of pharmaceuticals (Duc-Nam Luu, 2022; Van Nguyen,
2025) and their packaging. This approach is based on three complementary
strategies: (i) reducing the weight and/or volume of products and packaging,
(ii) adopting alternative packaging materials with a lower environmental
impact, and (iii) optimizing transportation methods to minimize the carbon
footprint associated with logistics (Bassani, 2022).
Table
1: Strategies for Improving Energy Performance in the
Pharmaceutical Industry*
|
Optimization
|
Reuse
|
Maintenance / Replacement
|
|
Advantages
|
- Improved efficiency
without major system changes
- Quick, low-cost
implementation
- Reduced energy losses
and long-term savings
|
- Reduced energy use by
recovering excess energy
- Lower operating costs
- Improved
sustainability
|
- Durable and
continuous improvements
- Reduced maintenance
costs and carbon footprint
|
|
Limitations
|
- Limited benefits on
outdated systems
- Requires control
equipment or software
- Delayed returns in
some cases
|
- Requires investment
in recovery systems
- Not applicable if the infrastructure is incompatible
|
- High initial
investment
- Potential downtime
during replacement
- Need for staff
training
|
|
Examples outside Arab countries
|
- Sanofi: HVAC
optimization (Sisteron) → 15% savings
- SALF: Smart sensors +
AI → 28% savings (Evogy, 2023)
- Sanofi: ISO 50001
certification for 30 sites (2021)
- J&J: Transition
to 100% renewable electricity (Johnson & Johnson, 2021)
|
- GSK: Heat recovery
from cooling water (Mayenne) → 70% of hot water needs covered (GSK,
2021)
- Merck MSD: 90
GWh/year of green electricity (TotalEnergies, Spain)
|
- Pfizer: Annual
investments of $25–40M in sustainable tech and heat recovery
- Douglas
Pharmaceuticals: Solar panels (Auckland) → −40% carbon footprint
(2020–2024) (Douglas_Pharmaceuticals, 2024)
|
|
Examples in Arab countries
|
- Sothéma (Morocco):
−13% via HVAC optimization + ISO 50001 strategy
- Maphar (Morocco): ISO
50001 certified since 2016
- FMIIP–IFC (Morocco):
Sectoral improvement via water/energy management (FMIIP-IFC, 2022)
|
- RAM Pharma (Jordan):
Heat recovery with Fresnel solar collectors → 30,000 L of diesel saved
annually (Haagen, 2015)
|
- GEFF (Egypt): New
pharmaceutical line → −33% energy and −240 t CO₂/year
(GEFF, 2024)
- Rameda (Egypt):
Partnership with IFC for greener production (Rameda-IFC, 2022)
|
Source: Authors’ own work
3.
Focus on Fluidized Bed Drying Processes
Dry dosage forms
represent the most widely marketed pharmaceutical formulations due to their
inherent stability, ease of storage, and high patient acceptability. Their
manufacturing processes typically follow a sequence of critical steps, among
which drying plays a central role.
Drying is a unit
operation that involves the thermal removal of volatile substances, primarily
moisture, to obtain a solid product. It entails a simultaneous transfer of both
energy and mass (Mujumdar, 2015; 2022). Among various drying technologies,
fluidized bed drying is widely employed in pharmaceutical manufacturing,
particularly for drying granules produced via wet granulation. This preference
stems from its high efficiency and the consistent product quality it enables (Parikh,
2017). However, this process also presents significant energy and operational
challenges. A typical fluid bed dryer operates through three distinct phases: (i)
Preheating of the equipment without product load, (ii) Active drying of the
granules, and (iii) a Cooling phase to stabilize the final product (Barriga,
2023). Each of these phases incurs substantial costs in terms of time, labor,
and particularly energy consumption, due to the need for air heating, forced
circulation, and ventilation. These factors constitute major drawbacks of fluid
bed drying systems (Barriga, 2023). Moreover, it is estimated that only 55–65%
of the energy input is effectively used to evaporate moisture, with the
remainder being lost (Majumder, 2022).
In contrast to the
relatively advanced optimization of HVAC systems, efforts to optimize fluidized
bed drying in pharmaceutical settings, especially in the Arab world, remain
limited, despite the availability of several tools described in the literature.
These tools include mathematical process modeling, data analytics, artificial
intelligence, and machine learning techniques, which are now widely used across
the pharmaceutical industry (Chen, 2023; Barriga, 2023; Diaz, et al., 2023;
Sampat, 2022; Aziz, 2022; Kim, 2020; Gavi, 2019; Ghijs, 2019; Sciuto, et al.,
2016). A study (Majumder,
2022) suggests that defining optimal process parameters can reduce energy
consumption by 10–30%. Another study (Barriga, 2023), which employed a
combination of exploratory data analysis (EDA) and a CatBoost machine learning
model, demonstrated that this approach could predict and achieve a 50.45–59.68%
reduction in preheating time, corresponding to an average 50.48–59.76% reduction
in energy consumption during this phase.
3.1. Limitations and
Gaps in Actions Undertaken, particularly in the MENA Region
Despite the growing
number of initiatives aimed at improving energy performance, their impact
remains limited by several structural and strategic constraints, particularly
in the pharmaceutical sector of the Arab world.
Efforts such as the Race
to Zero campaign have led to a significant increase in commitments within
the MENA region, with a reported growth of over 104% between 2022 and 2023 (Al
Mubarak, 2023). However, these advances are largely declarative, due to the
absence of established standards and independent verification mechanisms,
resulting in major disparities in the scope and credibility of transition plans
reported by companies (Al Mubarak, 2023).
Moreover, these
initiatives are often implemented in isolation, lacking both comprehensive
energy programs and integrated Energy Management Information Systems (EMIS),
thereby limiting the overall effectiveness of the actions undertaken, as shown
in (Figure 4). Yet, to achieve Sustainable Development Goal (SDG) 7.3, which
aims to double global energy efficiency by 2030, an annual improvement rate of
+3.8% is required from 2022 to 2030. This calls for a systemic and
sector-specific approach, particularly within the pharmaceutical industry.
Such
strategies have already been observed in other countries and regions, most
notably in Europe, through initiatives like the Health Sector Decarbonization
Plan (DGE, 2023) and efforts by the European Medicines Agency (EMA) to promote
sustainable production of new pharmaceutical products.
Fig. 4: Progress Race to Zero by monitoring the level of their
green program (CCNUCC, 2023)
In
addition to lacking standardization, regional pooling, and coordination, which
hinder the dissemination of best practices, the MENA region also suffers from a
funding shortage, similar to trends observed in other parts of the world. This
challenge is particularly critical for pharmaceutical manufacturers in the MENA
region, whose economic model is primarily based on the production of generic
medicines, which generate lower profit margins than branded drugs (Research
Expert Market, 2024). As a result, it becomes difficult to adapt
internationally developed solutions to the specificities of the Arab
pharmaceutical context. This situation is further exacerbated by limited
awareness and a scarcity of published research on energy optimization in the
region’s pharmaceutical sector.
This study is part of a
broader effort to advance the energy transition within the pharmaceutical
industry in the Arab world. Its objective is to promote energy efficiency
through the optimization of a process that is particularly widespread in local
pharmaceutical production facilities: drying. To the best of our knowledge,
this is the first study specifically dedicated to the energy optimization of
drying operations in the pharmaceutical context of the MENA region.
As such, this work offers
an original contribution, providing practical insights to help shape
industrial-scale energy efficiency policies, while also considering the
economic and technical constraints specific to countries focused on generic
drug production.
4.
Methodology
This project involved transferring a granulation process with
tray drying, which had reached the saturation point, to a workshop equipped
with a fluidized air bed (FBD) that had recently been installed but was underused.
The approach adopted considers economic, quality, and environmental
considerations, in line with the organization’s sustainable development policy,
to optimize the operating conditions of the process.
In this workshop,
dedicated exclusively to non-injectable dry forms, drying and the HVAC system
represent the main sources of energy consumption. In this article, we focus
solely on drying, as its energy impact far exceeds that of the other stages of
the transferred process. The aim is to determine the optimum drying parameters
for reducing energy consumption and operational costs, while guaranteeing
product quality.
The granulation line used
is Boch’s Granuleean 600, with its FGL 600 mixer, shown below. The initial
granulation line consists of a Collette IMH 450 planetary mixer, a drying oven,
a Frewit sizer, and a VMI agitator for preparing the fount solution.
Fig. 5: Représentation schématique
du FBD-FGL 600
Source: Authors’ own work
The properties of the
active substance of the drug are grouped in the table, and the estimated
properties of the wet granules are estimated via different correlations in the
following (Table 2).
Table
2: Characterization of active substance
|
AS
|
Terbinafine
|
|
Formula
|
|
|
Aspect
|
White or almost white
powder
|
|
Molar mass (g/mol)
|
327,9
|
|
Solubility
|
Very slightly or sparingly
soluble in water (solubility), freely soluble in anhydrous ethanol and
methanol, sparingly soluble in acetone
|
|
Melting point (°C)
|
205,0
|
|
Polymorphism
|
No
|
Source: Authors’ own work
Table
3: Characterization of wet granules
|
AS
|
Terbinafine
|
|
Water content
|
0,4
|
|
Heat Capacity
Coefficient-dry particle (J/kg.K)
|
1100
|
|
Density 
|
1000
|
|
Sphericity
|
0,8
|
|
Diameter (mm)
|
9
|
Source: Authors’ own work
Simulations and optimization are carried out on the drying stage, using
the two-phase fluidized air bed model implemented in MATLAB.
4.1.
Mathematical model
The two-phase fluidized
bed (FBD) model was chosen because it accurately represents the industrial
fluidized bed drying process, which is widely used in the pharmaceutical
industry due to its combination of energy efficiency and product quality. This
model precisely captures the interactions between the solid phase (particles)
and the gas phase (hot air), which is essential for optimizing heat and mass
transfer. The key assumptions include homogeneous flow, constant material
properties, and no changes in the particles during drying.
Description of the
model:
The two-phase model
(Figure 6) serves as a robust framework for elucidating the drying process of
pharmaceutical powders within fluidized beds. This model categorizes the volume
of the fluidized bed into two distinct phases:
·
The dense phase, which comprises particles and interstitial gas that
engage in interactions, thereby facilitating mass and heat transfer processes.
·
The bubble phase, characterized by voids filled with residual gas that
manifest as ascending bubbles within the fluidized bed.
Fig. 6: Schematic representation of
mass and energy transfer in the model,
adapted from (Gagnon, 2021; Lau, 2023)
The following differential equations
delineate the system’s dynamics:
·
·
(1)
·
(2)
·
(3)
·
(4)
·
(5)
·
(6)
·
(7)
·
(8)
·
The parameters and transfer
coefficients are given in (Table 4) and (Table 5).
Table 4: Correlation parameters
|
Parameters
|
Correlation
|
Operating
conditions
|
References
|
|
|
|
-
|
(Kunii, 1991)
|
|
|
|
|
(Kunii, 1991)
|
|
|
|
0,5<𝞥 <1
|
(Kunii, 1991)
|
|
|
|
|
(Kunii, 1991)
|
|
Δ
|
|
|
(HG Wang un, 2007)
|
|
|
|
-
|
(Kunii, 1991)
|
|
|
= (1-  )*
|
-
|
(Lau, 2023)
|
With: 
Table
5: Correlations transfer
coefficients
|
Coefficients
|
Correlations
|
References
|
|
|
|
(LimLaw, 2007)
|
|
|
|
(Mujumdar, 2015)
|
|
|
|
(Kunii, 1991)
|
|
|
|
(Kunii, 1991)
|
|
|
|
(Kunii, 1997)
|
|
|
|
(Kunii, 1997)
|
|
|
|
(Kunii, 1997)
|
|
|
|
(Kunii, 1997)
|
|
|
|
(Grewal &
Saxena, 1980)
|
|
|
|
(Osman,
Upadhyay, & Saxena, 1982)
|
|
|
|
(Churchill
& Chu, 1975)
|
With: 
if 
if 
Where:
4.2. Objective function and optimization constraints:
The objective
function maximizes profit, considering drying time and energy performance, and
the constraints are grouped in the (Table 6).
Table 6: Optimization constraints
|
Parameters
|
Lower bound
|
Upper bound
|
Justification
|
|
|
|
|
Speed range allowing fluidization
|
|
|
303,15
|
333,0
|
The temperature range is deemed
to pose an acceptable risk to the destruction of the substance
|
|
|
0,02
|
0,0001
|
The humidity range is easily
achieved and controllable with the available equipment
|
5.
Economic evaluation and environmental impact
The economic evaluation
of the three studied solutions is based on a comparative analysis of the energy
costs associated with the operation of the fluidized air bed drying system
(FAB). The following scenarios were considered:
- Scenario 1: Baseline
process using the original FAB parameters (prior to any intervention);
- Scenario 2: Standard
FAB parameters applied post-intervention, without specific optimization;
- Scenario 3: FAB
system optimized using a dynamic two-phase simulation model.
Energy consumption data
for Scenario 1 were measured on-site during actual drying cycles, while those
for Scenarios 2 and 3 were obtained via simulation using MATLAB. These energy
values were then converted into annual costs using the local electricity tariff
(1.2 MAD/kWh), assuming an average annual operation time of 2,400 hours. In
parallel, greenhouse gas emissions were estimated using conversion factors
provided by the Mohammed VI Foundation for Environmental Protection.
The results pertain
exclusively to the drying of wet granules, with a residual moisture content
estimated at 0.4, based on product formulation parameters described in the
manufacturing protocols.
Additional investments
were annualized over an assumed equipment lifespan of five years, using the
standard constant annuity formula with a discount rate of 5%.
6.
Findings and Analysis
7.
Simulation and Optimization:
Eight initial simulations
were carried out; the simulation parameters and results are presented in (Table
7). These simulations show that low temperature conditions (30°C) and high
input moisture content (0.017) result in long drying times and lower energy
efficiency. The process has a lower energy efficiency than that generally
obtained in practice, which is around 50% (Majumder, 2022). High temperatures
and speeds, with low inlet moisture, shorten the drying time, while low speed
and high temperature improve energy efficiency. Simulation 3, although better
in terms of energy consumption and efficiency, is sixth out of eight in terms
of drying speed. Optimizing the parameters means considering both energy
performance and the costs associated with a longer drying time, posing a
multi-objective optimization problem.
Table 7: Results of the initial simulation
|
N°
|
(°C)
|
(m/s)
|
|
|
 (j)
|
|
|
|
1
|
30
|
0,874
|
0,017
|
>133,33
|
|
0,45
|
0,04
|
|
2
|
60
|
0,874
|
0,017
|
55
|
138,03*
|
1,2
|
0,2
|
|
3
|
30
|
0,874
|
0,0001
|
77,1
|
97,44*
|
1,7
|
0,06
|
|
4
|
60
|
0,874
|
0,0001
|
41,3
|
100,4*
|
1,6
|
0,12
|
|
5
|
30
|
1,048
|
0,017
|
>133,33
|
>203,64*
|
0,4
|
0,04
|
|
6
|
60
|
1,048
|
0,017
|
53
|
162,1*
|
1
|
0,11
|
|
7
|
30
|
1,048
|
0,0001
|
75,66
|
114,9*
|
1,4
|
0,05
|
|
8
|
60
|
1,048
|
0,0001
|
38,7
|
118,22*
|
1,4
|
0,1
|
The optimum solution was determined
by two methods (graphical and using the multistat function), and simulated to
obtain its performance (Figure 7).
Fig. 7: Objective function response surface as a function of input
parameters (temperature and velocity)
The optimum solution is
the one with the following parameters: moisture content: 0.001; inlet
temperature: 333 K; speed: 1.5. In the simulation with these parameters, it can be seen that
the drying time required to reach the target moisture content (0.03) is 38.09
minutes, with an energy consumption of 160.55 × 10⁶ Joules. The
temperature and humidity curves show that beyond 2500 seconds, the moisture
content remains almost constant, while the temperature of the particles and the
container increases. This suggests that the energy supplied beyond this time is
used primarily for superheating the particles and vessel, rather than for
drying.
It is also observed that
before reaching the target point, the temperature of the vessel decreases. This
reflects the loss of energy stored by the equipment during the FBD preheating
phase at 323 K. This observation suggests that the FBD preheating temperature
is not optimal. By adjusting the inlet temperature to 305 K, which would make
it possible to maintain a constant vessel temperature before reaching the
target point, it would be possible to reduce energy losses during preheating.
8.
Economic evaluation and environmental impact
In this section, three
possible technical solutions have been defined, two of which serve as a basis
for comparison with the optimal solution defined. These possible solutions are
listed in Table 10. As solution 1 (with drying under a tray) is not a simulated
solution, the energy consumption required is that of the whole equipment
(product of the equipment power and the drying time). The calculation of
electrical energy consumption considers that of the oven, the FBD, and the
dehumidifier in cases where a humidity of 0.0001 is required. In addition, the
assessment is based on the number of annual batches of Terbinafine alone (24
batches/year), which represents only a tiny fraction of the annual production
capacity of the plant, so percentage comparisons are the most relevant.
In this section, three
possible technical solutions have been defined, two of which serve as a basis
for comparison with the optimal solution defined. These possible solutions are
listed in (Table 8). As solution 1 (with drying under a tray) is not a
simulated solution, the energy consumption required is that of the entire
equipment (product of equipment power and drying time). The calculation of
electrical energy consumption considers that of the oven, the FBD, and the
dehumidifier in cases where a humidity of 0.0001 is required. In addition, the
assessment is based on the number of annual batches of Terbinafine alone (24
batches/year), which represents only a tiny fraction of the annual production
capacity of the plant, so percentage comparisons are the most relevant.
Table
8: Feasible technical solution
|
N°
|
Possible
solutions
|
(m/s)
|
 (°C)
|
|
|
 (Kw)
|
 (j)
|
 (l)
|
|
1
|
Scenario 1 (Initial situation)
|
-
|
60
|
0,017
|
21
|
10
|
-
|
54
|
|
2
|
Scenario2 (FBD + classic
parameters)
|
0,96
|
30
|
0,017
|
4,69
|
2,5
|
444,63
|
555
|
|
3
|
Scenario 3 (FBD + optimized
parameters)
|
1.5
|
60
|
0,0001
|
0,633
|
2,5+
20,05
|
160,55
|
555
|
It was observed that, when accounting
for the energy consumption of both the equipment involved and the associated
utilities, a reduction of 56% in total energy use was achieved, reaching up to
70% compared to Scenario 1.
·
Economic evaluation:
The only additional investment considered
was the purchase of a calibrator, costing 2,470.00 MAD. This cost was
annualized over the estimated lifespan of the equipment (5 years). All
financial values are referenced to the base year 2024 and expressed in constant
dirhams. The comparative economic analysis demonstrates that Solution 3
(optimized FAB parameters) provides the best economic performance (Table 9), with a reduction in operational energy
costs of 9.5% and 58.6% compared to Solution 1 (initial state) and Solution 2
(standard FAB configuration), respectively.
Table 9: Summarizes the cost data
associated with each solution
|
Possible solutions
|
|
|
|
|
% Energy cost
|
% Cost of labor
|
% Investment
|
|
1
|
0
|
0
|
20395,77
|
20395,77
|
30,18%
|
76,49,1%
|
0
|
|
2
|
2479,2
|
396,08
|
44177,51
|
44573,59
|
63,94 %
|
35,00%
|
0,89%
|
|
3
|
2479,2
|
396,08
|
18061,23
|
18457,31
|
12,90%
|
84,51%
|
2,14%
|
·
Environmental Impact
Greenhouse
gas (GHG) emissions are quantified by multiplying the amount of each emission
source by its respective conversion factor. The emission factors utilized in
this calculation are sourced from the Mohammed VI Foundation for Environmental
Protection (fm6e, 2023). The findings are summarized in (Table 10) below. To
facilitate impact assessment across a comprehensive range of scenarios, the
worst-case FBD solution has been introduced. Notably, Solution 3, characterized
by optimized FBD drying, demonstrates superior performance, yielding an
emission level of 0,879 
, compared to the initial
solution at 3,696
and the final solution
employing standard parameters used within this workshop at 1628,57 . This indicates a
significant reduction in GHG emissions of 80% and 50%, respectively, on drying.
These results stand in contrast to existing literature, which posits that tray
drying exerts a lesser environmental impact; this discrepancy can primarily be
attributed to variations in the energy mix across different countries.
Specifically, energy production methods, particularly for electricity, vary
significantly between nations, leading to corresponding differences in
conversion factors; for instance, Morocco has a conversion factor of 0.735
while France’s is substantially lower at 0.08.
Table 10: Emissions
by Scope and Technical Solution
|
Scope
|
1
|
2
|
3
|
Total (kg/batch)
|
Total (kg/year)
|
|
Emission Source (Conversion
Factor)
|
Electricity (kWh) (*0.735)
|
Fuel (kg) (*3.14)
|
Water ( (*0.262)
|
|
Quantity
|
Emission
|
Quantity
|
Emission
|
Quantity
|
Emission
|
|
Solution 1
|
210
|
154,35
|
0
|
0
|
54.10-3
|
0,0142
|
154,01
|
3696,24
|
|
Solution 2
|
18,56
|
13,64
|
17,22
|
54.07,30
|
555.10-3
|
0,1464
|
67,86
|
1628,57
|
|
Solution 3
|
23,06
|
16,95
|
6,21
|
19,524
|
555.10-3
|
0,1464
|
36,62
|
878,89
|
9.
Discussion
This
study proposes an innovative approach to improving energy performance in
pharmaceutical drying through advanced modeling of fluidized bed drying
systems. By integrating a two-phase model, this method allowed the
identification of optimal process parameters, leading to a reduction in energy
consumption by –56.49% and –72.08% compared to Scenarios 1 and 2, respectively,
and a decrease in greenhouse gas emissions by 76.24% and 46.05% compared to the
same scenarios. These reductions in energy use and emissions were accompanied
by operational cost savings of 9.5% and 58.6% relative to Scenario 1 (initial
state) and Scenario 2 (standard FAB configuration). This performance
demonstrates that transitioning to cleaner pharmaceutical production does not
necessarily compromise economic outcomes; on the contrary, it can deliver
substantial operational gains.
Our results are
consistent with the findings of Majumder (Majumder., 2022), who showed that
strict control over drying parameters can reduce energy consumption by 10% to
30%. More ambitiously, Chen (Zhengyun, 2024) reported savings of up to 71.7% in
optimized batch production and 80% in continuous production using Process
Analytical Technologies (PAT). However, our study goes further by integrating a
multi-criteria analysis, simultaneously considering energy consumption, drying
time, and labor costs, an approach rarely addressed collectively in the
existing literature.
Another key strength of
this study lies in the transferability of the methodology: the use of a
simulation-based model makes it possible to adapt the system to other
industrial configurations or product types, without being exclusively reliant
on empirical trials. This ability to generalize is particularly critical for
pharmaceutical manufacturers in the MENA region, who often face specific
economic and technological constraints.
These actions, while
already contributing to energy reduction, can be further enhanced by
incorporating renewable energy sources, particularly in countries with high
renewable energy potential. A recent study (Ibrahim, 2024), focused on hybrid
solar dryers (HSD) applied to medicinal plants (lavender, basil, lemongrass,
etc.), demonstrated that combining solar energy (via photovoltaic panels) with
conventional electricity could reduce energy consumption by 37% to 54%, with a
corresponding decrease in CO₂ emissions (up to 54% depending on the
operating temperature), all while preserving product quality (essential oil
content and microbial load optimized at 40°C). Even more promising,
the exclusive integration of photovoltaic power (HSD-PVSE) led to a 100%
reduction in CO₂ emissions and energy costs, with an annual financial
yield estimated at over 15,000 EGP depending on the substituted energy source.
These findings offer valuable perspectives for pharmaceutical drying processes,
where energy requirements and operating temperature ranges can often be met
through solar energy.
The convergence of such
findings across different sectors highlights that a strategy combining
low-carbon technologies with technical modeling is a relevant path for energy
transition in the pharmaceutical industry. In Spain, for instance, up to 70% of
the energy used by the pharmaceutical sector is of renewable origin, according
to Farmaindustria’s Director General, Juan Yermo (Farmaindustria, 2023).
Despite these promising results, several limitations must be acknowledged.
First, the current model relies on simplifying assumptions, such as uniform
temperature and lack of product/process feedback, and on estimated granule
parameters based on correlations. Moreover, implementing such approaches
requires digital simulation expertise and energy metering infrastructure, which
remain scarce in many pharmaceutical facilities across the MENA region. In this
case study, the initial investment was modest because the process transfer was
made from a tray dryer to an already installed fluidized bed dryer. However,
this may not be the case in other facilities, where higher capital expenditure
may pose a barrier. Additionally, this work focuses solely on drying and does
not fall within a full life cycle assessment (LCA) framework.
Future analyses should
include scenarios that integrate alternative energy sources, such as solar
thermal collectors or electrification via renewables, and extend the scope to
encompass the full life cycle of pharmaceutical products for a more comprehensive
assessment of environmental impact. Energy hybridization in pharmaceutical
drying, mirroring approaches in the agri-food sector, represents a promising
avenue for enhancing the energy resilience of manufacturing sites.
This research highlights
the value of an integrated optimization approach to pharmaceutical fluidized
bed drying, combining dynamic modeling, carbon footprint reduction, and
economic improvement. It underscores the benefits of a sector-specific strategy
tailored to the industrial realities of the MENA region, and calls for deeper
exploration of disruptive technologies such as hybrid solar dryers and
renewable energy integration into critical pharmaceutical processes.
10. Policy discussion and recommendations
The results of this study
highlight the importance of process-specific operational optimization in the
energy transition of the pharmaceutical industry. Specifically, the
optimization of fluidized bed drying (FBD), as implemented here, enabled up to
a 58.6% reduction in operational costs and a 76% decrease in greenhouse gas
(GHG) emissions, all while maintaining pharmaceutical product quality. These
findings demonstrate that environmental and economic gains are achievable
without major structural reform, provided the right technical and operational
levers are targeted.
Based on these findings,
we propose the following recommendations, organized by intervention level and
stakeholder, following a logic of progressive scaling, from immediate
procedural adjustments to systemic reforms.
10.1. Operational level: Direct improvement of existing processes
Stakeholders:
Pharmaceutical manufacturers (production, engineering, and quality teams)
- Conduct targeted
energy diagnostics by consumption unit, focusing particularly on thermal
processes such as drying, HVAC, and compressed air systems.
- Use modeling and
simulation tools (e.g., two-phase fluidized bed models) to determine
optimal drying conditions (temperature, humidity, air velocity) tailored
to the products being manufactured.
- Train operators and
engineers in the interpretation of critical energy parameters and in
preventive maintenance of drying equipment.
Our results show that
simple adjustments of input parameters (velocity, temperature, humidity) can
drastically reduce energy consumption and associated costs, which is in line
with the findings from the literature.
10.2. Organizational level: Internal structuring of energy management
Stakeholders:
Pharmaceutical industry executives
- Implement energy
management systems aligned with ISO 50001, including performance
indicators by equipment (e.g., kWh per kg of dried product).
- Develop dedicated
investment plans for energy efficiency, including the replacement of
obsolete equipment (e.g., FBD units, dehumidifiers, steam generators).
- Systematically
integrate energy audits into internal quality audits and GMP or
environmental certification processes.
The adoption of a
structured energy management approach ensures the sustainability of
operational-level gains while enhancing environmental compliance during
certification audits.
10.3. Sectoral level: Incentives and support for energy transition
Stakeholders:
Governments, health authorities, industrial federations
- Establish financial
incentive mechanisms (e.g., subsidies, tax credits, energy savings
certificates) for companies integrating low-carbon solutions (e.g., hybrid
dryers, electrification of processes).
- Develop a regional
industrial energy performance database, including pharmaceutical sectors,
to guide policies and facilitate cross-country benchmarking.
- Incorporate energy
performance criteria into pharmaceutical compliance programs, similar to
the EMA’s sustainability requirements.
Pharmaceutical
manufacturers in the MENA region often operate in cost-constrained
environments. Regulatory and economic incentives are therefore essential to
offset the initial cost of adoption.
10.4. Academic and R&D level: Supporting process innovation
Stakeholders:
Universities, research centers, funding agencies
- Support applied
research projects aimed at modeling, optimizing, and electrifying
energy-intensive processes, particularly drying, through targeted funding.
- Create
academia-industry partnerships to co-develop digital energy management
tools at the process level (e.g., digital twins, predictive AI, dynamic
LCA).
- Integrate modules on
energy sustainability into curricula for industrial pharmacy, process
engineering, and pharmaceutical management programs.
The lack of technical
expertise has been identified as a key barrier to the on-site adoption of
energy performance innovations. Applied research and education can bridge this
gap.
Therefore, rather than
initiating heavy reforms or regulatory overhauls, this study advocates for a
progressive, pragmatic, data-driven approach. In this context, the optimization
of drying processes serves as an entry point for a broader energy transformation
of the pharmaceutical industry. Coordinated efforts among industry
(implementation), government (incentives), and academia (innovation) are
essential for a successful transition toward more sustainable, resilient, and
competitive pharmaceutical manufacturing.
11. Conclusion
The rational
management of energy in the pharmaceutical industry is now a strategic lever
for ensuring both economic and environmental sustainability, particularly in
the context of a historical dependence on fossil fuels in the Arab world. This
study has highlighted the sector’s specific energy challenges, as well as local
and international initiatives aimed at optimizing energy consumption without compromising
drug quality and safety.
Our findings confirm that energy
performance data for the pharmaceutical sector remain fragmented, especially in
Arab countries, in contrast to better-documented industrial sectors such as
agri-food. This underscores the urgent need to strengthen applied research,
document existing practices, and implement systematic energy audits, as already
mandated by the legislative frameworks of some countries, such as Morocco. The
case study conducted on the optimization of pharmaceutical drying, a highly
energy-intensive process, concretely illustrates that targeted technical
adjustments can lead to significant reductions in energy consumption (up to
58.6%) and greenhouse gas emissions (up to 76%) without requiring major
investments, while maintaining pharmaceutical quality standards. These findings
demonstrate the relevance of integrating energy analysis into the development
and industrial transfer phases of pharmaceutical production. However, the
effectiveness of such initiatives remains limited in the absence of a structured,
sector-wide strategy. The diversity of approaches, the lack of standardized
indicators, and the risk of greenwashing hinder inter-site comparisons and the
monitoring of performance. It is therefore imperative to define a
sector-specific framework for pharmaceutical manufacturing, including: (i)
harmonized indicators, (ii) clear functional units, and (iii) integrated energy
management systems (such as ISO 50001).
Based on this framework, three
complementary pathways for action can be proposed:
·
Short-term: Promote on-site operational
optimization through procedural adjustments, targeted training, and energy
diagnostics.
·
Medium-term: Structure energy management at
the facility level through the implementation of standardized systems,
supported by incentive policies (e.g., subsidies, tax relief).
·
Long-term: Drive a structural transformation
of the sector through the creation of energy performance databases, integration
of sustainability criteria into pharmaceutical regulations, and development of
cross-sectoral models inspired by other industries.
Finally, this study opens concrete
research perspectives to support the transition:
·
Assessing the energy maturity of
pharmaceutical manufacturing facilities;
·
Analyzing the sector’s carbon footprint
through a life cycle assessment (LCA) approach;
·
Testing integrated energy management models
tailored to the industrial realities of the Arab world.
In conclusion, this study demonstrates that
it is possible to make the Arab pharmaceutical industry greener, more
competitive, and more resilient, provided that local action is rigorously
linked to a coordinated global strategy.
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