The world’s energy demand is projected to increase by nearly 50% by 2050 [11] due to a rise in the global population and increased economic activities. According to the 2023 energy statistic, renewable energy will account for 65% of all primary energy supplies to the world by 2050, including the majority of renewable energy supply (biofuels and waste-derived bioenergy). Transportation will consume 24.7% of total energy consumption [12]. However, the development of renewable energy markets, such as biofuels, is largely driven by national support policies, including mandates for blending into petrol and diesel. To put it succinctly, vegetable oil can qualify as an advanced biofuel under the Renewable Fuel Standard (RFS) rules (OECD-FAO 2023), and biofuels like biodiesel possess comparable properties to fossil diesel.

Biodiesel can be produced through several pathways, including pyrolysis, microemulsion, supercritical processes, and transesterification [13]. In commercial practice, biodiesel production is commonly carried out in batch or continuous modes [8]. Among these routes, transesterification remains the most widely used because it is relatively simple, and capable of producing high yields. Both homogeneous and heterogeneous catalysts have been applied in transesterification, and the process may proceed through acid-catalyzed, base-catalyzed, or non-catalytic routes [14].

Although biodiesel offers environmental benefits and supports the palm oil downstream industry, its wider adoption in Malaysia is constrained by economics. A major barrier is the country’s subsidized diesel policy [15], which keeps conventional diesel prices low and reduces the commercial incentive for producers to expand biodiesel output. In addition, biodiesel production remains feedstock-intensive, and feedstock costs account for a substantial share of total production expenses i.e. raw material cost accounts for about 75% - 90% of the total biodiesel production cost [16]. As a result, the high cost of biodiesel production, combined with relatively cheap subsidized fossil diesel, weakens producer motivation to invest in biodiesel ventures. This challenge limits the advancement of palm-based biodiesel production despite Malaysia’s strong palm oil industry and its potential contribution to value-added downstream development.

However, the unit cost of biodiesel production can be lowered by increasing process productivity, reducing feedstock cost, and minimizing utility demand. Recent literature indicates that improved production methods can enhance biodiesel yield and process efficiency, while an effective energy management scheme can significantly reduce utility expenses [17].

Figure 1 shows the process flow diagram of vegetable oil transesterification in one of the Malaysian commercial biodiesel plants. The feedstock of refined, bleached, and deodorized palm oil (RBDPO) is fed into a dryer for dewatering and degassing purposes. The RBDPO continuously feeds into a mixer, excess methanol with 99.9% purity, and catalyst dose is injected into the mixer for well-mixing purposes before it is fed into Reactor 1. as excess methanol drives the reaction forward in accordance with oil transesterification stoichiometric reaction shown in Equation (1).

Transesterification is a reaction to react 3 moles of methanol with 1 mole of oil or triglyceride to produce 3 moles of biodiesel and 1 mole of glycerol (glycerin). Removing the glycerol continuously from the reactors is needed for ensuring high biodiesel yield by shifting the reaction forward. Reactors 1, 2 and 3 are equipped with a circulation pump to enhance the reaction rate of oil transesterification by mixing methanol into the oil [13][18]. The mixer and circulation pump allows keeping certain mixing degrees inside the reactors.

Figure 1.Typical commercial biodiesel plant process flow diagram of palm oil transesterification.

Excess methanol which more than stochiometric need to supply into the transesterification reaction, aims to shift the reaction forward. After the biodiesel process is almost complete in the reactor, the excess methanol and crude biodiesel and glycerol is discharged. After separation, methanol is available for recovery and reuse by using the methanol rectification column. The liquefied methanol is then fed directly into the methanol rectification column.

The rectification process yields methanol with a purity of 99.9%, which is collected via the overhead condenser. Part of this methanol is returned to the column as reflux, while the remainder is discharged into a storage tank for feeding into the reactor. The column operates under slight atmospheric pressure, maintaining a temperature of 65˚C at the top of the column and 100˚C at the bottom stream.

Recycling methanol is energy intensive providing an opportunity for energy efficiency opportunities evaluation during biodiesel operation [19]-[21]. According to the 2009 USA National Biodiesel Board, the energy consumption for industrial biodiesel production is an average of 4.4 MJ/gal of biodiesel [20], with plant capacity unknown. Energy consumption for algae oil and waste cooking oil was 1.94 MJ/gal biodiesel and 1.94 MJ/gal biodiesel, respectively. Energy for methanol recovery in biodiesel processes accounts for up to 50% of the plant operating cost [19].

Software solutions for process simulation, integration, and optimization have been widely employed, assisting process sector businesses in meeting their operational objectives. There are numerous effective tools accessible, each with its own set of advantages. Table 1 displays the market’s current commercial software program bundle.

Table 1.Process simulator software. Adopted from chemical engineering process simulation [22].

Process simulation which first appeared in the 1960s, uses computer-based mass and energy balance computations for steady-state processes [22]-[24] described process simulation as a mathematical model that represents the processes of a chemical plant and facilitates data sourcing through computer aid.

Process modelling is under the purview of process synthesis. it is a reliable method for improving current processes at a cheap cost [25].

Balance reconciliation and flowsheeting simulation are extensively used for sustainability design and savings analysis, establishing themselves as important assets in a process engineer’s toolset [26]. Flowsheets are optimal processing system and unit design arrangements [27]. Flowsheet synthesis employs a three-level hierarchical approach: synthesis optimization, design optimization, and operational optimization [28]. Flowsheet evolves through iterations of major equipment blocks and allows for the specification of specifics for both new and existing plants, such as flow rates, temperatures, and pressures. Additionally, it is subdivided further (process analysis) to evaluate the effectiveness of each component [22].

AspenTech [29] provides integrated process simulation software for industries such as oil and gas, chemicals, and oleochemicals. Aspen Plus is widely used for process modelling, conceptual design, optimization, and performance monitoring. It includes extensive databanks for pure-component properties and phase-equilibrium data for conventional chemicals, electrolytes, solids, and polymers. The software also supports rigorous sizing and rating of key equipment, including heat exchangers and distillation columns, within the simulation environment. Hill and Justice [30] reported that commonly used thermodynamic property methods in Aspen Process Simulator are summarized in Table 2.

Table 2.Thermodynamic property set model category and its commonly used models, adopted from [30].

The following researchers have advised using caution while selecting thermodynamic property models. For example, [31][32] used non-random two liquids (NRTL) models or Universal Quasi Chemical (UNIQUAC) models as thermodynamic models because methanol and glycerol are polar components, and NRTL-SRK and UNIQUAC-SRK models were used for equipment where the vapour-liquid equilibria were calculated. However, researchers such as [29][33][34] have reported using Dortmund modified UNIFAC for biodiesel process simulation. Their findings show that the models are capable of predicting the physical attributes of the components under consideration. The summary depicted in Table 3.

Table 3.Summary of thermodynamic models used in aspen simulation.

Even if the literature on oleochemical energy management is limited on unit operation application [44][45]. However, Zarlie [46] has supplied an overview write-up for oleochemical distillation and fractionation procedures. He stated that because fatty acids are heat instable, designing a distillation machine for thermolabile fatty acids required caution. To prevent cracking or polymerization, high vacuum and low temperature conditions are used. It is critical to have a short residence time at the column bottom. Deaerators, thermal oil, or high-pressure steam are used as heat sources in distillation processes, along with condensing and vacuum. Overall, it is an energy-intensive procedure. Indeed, as shown in Table 4 there are emerging literatures in the Malaysian oleochemical industry [45][47][48].

Energy management implementation guided by its framework and standards. It provide a structure for conducting energy reviews and audits. These processes allow for the analysis of energy consumption, assessment of heat recovery potential, and identification of opportunities for energy savings [49]. EN 16001 [50] is the energy management system of the European standard published in 2009. It complies with ISO 14001 and is based on the plan-do-check-act cycle. EN 16001 aids organizations setting up a comprehensive energy management system and regularly improve their energy utilization performance, leading to lower energy costs and fewer greenhouse gas emissions [50].

ISO 50001 consists of five phases which emphasize continual improvement; policy, planning, implementation, checking and corrective action and management review. Adoption of the ISO 50001 framework in an organization is one of the options for energy management to consider [51]. It is a plan-do-check-act cycle management system which is designed to follow the Deming quality management system.

Although literature on oleochemical energy management remains limited, recent studies have begun to address energy use and waste heat recovery in the Malaysian oleochemical industry. Zarlie [46] provided an overview of oleochemical distillation and fractionation, noting that fatty acids are thermally sensitive and therefore require careful column design under high-vacuum and low-temperature conditions to minimize cracking and polymerization. Because distillation in this sector is energy intensive, utilities such as thermal oil, high-pressure steam, condensers, and vacuum systems are commonly used. Khong [52] has provided literature gap in oleochemical section for energy management typically in waste heat recovery, however, the current literature still provides limited guidance on decision frameworks for energy management, particularly for evaluating waste heat recovery opportunities in biodiesel. This gap warrants further investigation within the Malaysian oleochemical industry. Table 4 illustrates the research on energy management and waste heat recovery in the Malaysian oleochemical industry.

Energy management systems provide a framework for energy reviews and audits, helping organizations identify energy-saving opportunities and waste heat recovery potential. EN 16001 [50], published in 2009, was an early European standard based on the plan-do-check-act cycle. It has since been superseded by ISO 50001, the current international standard, which supports continual improvement in energy performance through policy setting, planning, implementation, checking, and management review [51].

Although literature on energy management in oleochemical unit operations remains limited, recent studies have begun to address energy use and waste heat recovery in the Malaysian oleochemical industry [44][45]. Zarlie [46] provided an overview of oleochemical distillation and fractionation, noting that fatty acids are thermally sensitive and therefore require careful column design under high-vacuum and low-temperature conditions to minimize cracking and polymerization. Because distillation in this sector is energy intensive, utilities such as thermal oil, high-pressure steam, condensers, and vacuum systems are commonly required.

Energy management systems provide a structured framework for energy reviews and audits, enabling organizations to assess energy consumption, identify heat-recovery potential, and determine opportunities for energy savings. Early standards such as EN 16001 were based on the plan-do-check-act cycle and later informed the development of ISO 50001, which remains the international benchmark for continual improvement in energy performance through policy development, planning, implementation, checking, and management review [51].

Table 4Emerging energy management research works undertaken for Malaysian oleochemical firms.

However, current literature still provides limited guidance on how such frameworks can be applied to Malaysian oleochemical plants, particularly for evaluating waste heat recovery opportunities in biodiesel-related processes. Khong [52] also highlighted the limited research on energy management in the oleochemical sector, especially in relation to waste heat recovery. This gap warrants further investigation into energy management strategies for the Malaysian oleochemical industry. Table 4 depicted the Emerging energy management research works undertaken for Malaysian oleochemical firms.

Energy efficiency is commonly defined as the effectiveness with which energy resources are converted into useful work [53]. In industrial settings, improving energy efficiency requires structured reviews of energy consumption through audits and assessments, which can reveal opportunities for heat recovery and other energy-saving measures. However, technical solutions alone are not sufficient. Successful implementation of energy-efficiency programs also depends on organizational commitment, staff engagement, and system knowledge. Recent studies on ISO 50001 implementation indicate that these managerial and behavioral factors remain essential for effective energy management and continual improvement in energy performance [54].

Energy-efficiency improvement is often a complex and time-consuming process that typically begins with energy audits, consumption mapping, and identification of improvement measures. The commitment of management, employee awareness of energy wastage, knowledge of the system under study, and support from internal or external experts are key factors for successful implementation [49][54][55]. In addition, efficient energy use is a cost-effective strategy that can reduce CO₂ emissions, improve productivity, and lower dependence on imported energy sources [56].

Although research on energy efficiency has grown in recent years, gaps remain in the methods used to assess and improve industrial energy performance [57]. Previous studies have examined energy-intensive motor systems, the integration of energy management with pinch analysis in petrochemical plants, and the use of intelligent energy-saving systems in industrial buildings [49][58][59]. However, fewer studies have applied process simulation tools such as Aspen Plus to evaluate energy efficiency at the unit-operation and plant level, particularly in oleochemical and biodiesel production systems. Aspen simulation is important because it enables detailed modeling of mass and energy flows, identification of high-consumption equipment, and assessment of heat-recovery opportunities under realistic operating conditions. In this way, Aspen Plus provides a rigorous framework for translating energy-efficiency concepts into practical, process-specific improvement strategies.

The overall methodological framework for this study is presented in Figure 2. However, this case study focuses only on Phase 1, which involves the process simulation and validation of the biodiesel plant.

Figure 2.Overall methodology framework.

Aspen simulation for the commercial biodiesel plant starting with registration of new components (triglyceride, methanol, glycerol and biodiesel) in the properties section then followed selection of thermodynamic model to enable physical properties estimation of simulation. Dortmund modified UNIFAC (UNIF-DMD) and None Two Random Liquid (NTRL) have been selected as the thermodynamic model used in this study. Literature review showed that these two thermodynamic models has been widely used by the researcher like Zhang [31] and Granjo [39] and Azad [43].

Following is the unit models built according to Sequencing of unit models method for easier managing the complex flowsheet [22]. These models are built according to the mass balance illustrated in Figure 3, the simulation model iteratively until converged.

Figure 3.The overall mass balance of the commercial biodiesel plant with 176,000 TPA as oil input.

The process simulation model was divided into subsections, as shown in Figure 4, and each subsection was developed and run until convergence. Subsequently, the overall biodiesel model was established after combined all the sub-model, lastly the recycle methanol stream was developed till convergence. The simulation model was then brought to the DCS for model validation iteratively with small adjustment until process parameter and quality complied as depicted in Figure 4.

Figure 4.Phase 1—process simulation methodology framework.

The plant data used for model validation were collected over a one-year operating period following the successful commissioning of the biodiesel plant by the in-house Process Commissioning Team. More than ten steady-state operating cases were selected and evaluated during the validation process.

Stable plant operating conditions were defined as periods with no interruptions to feed supply, electrical power, distributed control system (DCS) operation, or normal plant operations. During these periods, all major process variables, including pressure, temperature, mass flow rate, and liquid level, remained within their normal operating ranges without significant fluctuations.

For each steady-state case, the simulation model was iteratively adjusted and validated against measured plant data using key process parameters (pressure, temperature, mass flow rate, and liquid level) and critical product quality indicators, such as methanol content in biodiesel and glycerol streams. The validation procedure was repeated until satisfactory agreement between the simulation results and plant data was achieved.

Only the models for the major fuel-consumption units are presented here. The complete model, together with its validation, is available upon request for academic purposes. This limitation is intended to protect the intellectual property of the technology provider. The unit models listed are:

a) Methanol rectification column (Figure 5(a));

b) First methanol stripper for crude biodiesel (Figure 5(b));

c) Second methanol stripper for refined biodiesel before the storage tank (Figure 5(c));

d) Methanol flasher for crude glycerol before the storage tank (Figure 5(d)).

These unit models are constructed as shown in Figures 5(a)-(d).

(a)

Figure 5.(a) Typical methanol rectification column. (b)-(d) Typical methanol removal unit for biodiesel and glycerol.

The Aspen Plus V10 simulation was developed by first registering the key process components, including triglycerides, methanol, glycerol, and biodiesel (FAME), in the Properties environment. Thermodynamic properties were estimated using the Dortmund Modified UNIFAC (UNIF-DMD) and Non-Random Two-Liquid (NRTL) property methods. UNIF-DMD was applied to the transesterification section involving triglycerides, biodiesel, methanol, and glycerol, while NRTL was used for the methanol recovery and purification sections.

The transesterification reactor was represented as a reactor vessel without explicitly modeling the reaction kinetics or stoichiometric conversion equations for simplicity as the reaction is not the primary analysis in this case study. Key distillation-column specifications, including operating pressure, reflux ratio, and product purity targets, were based on actual plant operating conditions. The simulation model was subsequently validated using industrial plant data and used for the energy-analysis study.

The Aspen Plus V10 model was validated using plant operating data collected during stable operating conditions. Validation was performed by comparing simulated and measured values for key process variables, including major equipment heat duties, biodiesel and glycerol flowrates, methanol recovery purity, biodiesel methanol content, and glycerol quality. The percentage error for each variable was calculated relative to the corresponding plant measurement.

For example, the accuracy of the model for the first methanol stripper was evaluated using the Mean Absolute Percentage Error (MAPE), as defined in Equation (2). Table 5 presents the plant and simulation data, together with the corresponding percentage errors, for the first methanol stripper. The results showed satisfactory agreement between the simulation model and the industrial plant data, confirming the suitability of the model for energy-calculation studies. The calculated MAPE was 1.48%. All the validation is performed in these manners.

Mean Absolute Percentage Error (MAPE)

Table 5. Comparison of plant data and simulation data.

The specific energy consumption for the baseline case was determined to be 200 kWh per metric ton (MT) of oil input based on the process simulation results at 500 MPD plant capacity. This value was obtained under steady-state operating conditions using the validated simulation model of the biodiesel production plant at an oil-to-methanol ratio of 1:5. Martin and Grossmann [20], citing data from the 2009 National Biodiesel Board, reported an average industrial energy consumption of approximately 4.4 MJ/gal biodiesel for unknown plant capacity, equivalent to about 367 kWh/MT biodiesel. The baseline case developed in this study showed a specific process energy consumption of 200 kWh/MT oil input at 500 MPD. Direct comparison should be interpreted with caution because of differences in the calculation basis and process boundary definitions.

The operating basis corresponds to the baseline process configuration with the original energy optimization setup provided by the technology provider. The reported energy consumption was calculated from the simulated process energy requirements, including the heating and cooling duties of the process units. Electrical power consumption associated with equipment such as pumps, motors, and auxiliary utilities was not considered in this calculation. Therefore, the reported value represents the process energy demand predicted by the simulation model for biodiesel production from the specified oil feedstock.

Table 6. Comparative heat loads at an Oil: Methanol Molar Ratio of 1:5 used in the transesterification reactor. (Given dataset and simulated dataset)

After operating the biodiesel plant for three years, several valuable insights have been identified for dissemination to oleochemical industry stakeholders to support lower energy consumption through energy management, without incurring additional expenditure as listed below.

5.2.1. Oil to Methanol Ratio

After the plant reached stable operation, the process engineering team focused on the methanol purification column, which is the largest energy consumer in the biodiesel production process. As shown in Table 7, energy savings were evaluated by reducing the methanol feed ratio to the transesterification reactor while maintaining transesterification performance and product quality.

The simulation results indicate that reducing the oil-to-methanol ratio from 1:5 to 1:4.5 achieved an energy saving of 7.6% compared with the baseline case. At the same time, the biodiesel yield remained at 98.5% conversion, with no significant deterioration in product quality. The biodiesel product continued to satisfy the export-grade requirements specified in EN 14214. These results demonstrate that the lower methanol ratio can be implemented without compromising transesterification performance while providing a measurable reduction in process energy consumption.

Table 7. Heat Loads at an Oil: Methanol Molar Ratio of 4.5 and 6:1 in the transesterification reactor.

5.2.2. Production Planning and Scheduling

Production planning and scheduling is the second key action that directly affects energy management for the process engineering team. Starting up production or changing over feedstock, which interrupts production stability and may produce off-specification product, will erode the business viability of the biodiesel plant. Detailed production planning is pivotal to the biodiesel plant.

5.2.3. Optimization of Refine Biodiesel Using Validated Simulation Model

By diverting a portion of the mass flowrate to the reflux stream (Table 8), the total heat load can be calculated with methanol content and operating pressure held constant. The results show that the heat load ranges from 1080 to 1097 kW, which indicates that heat load is not the primary optimization point in biodiesel plant. Exploring other factors is therefore vital.

Table 8.Biodiesel - methanol stripper.

5.2.4. Optimization of Refine Glycerine Using Validated Simulation Model

By diverting a portion of the mass flowrate to the reflux stream (Table 9), the total heat load can be calculated with methanol content and operating pressure held constant. The results show that the heat load ranges from 883 to 884 kW, which indicates that heat load is not the primary optimization point in the biodiesel plant. Exploring other factors is therefore vital.

Table 9.Glycerol - Methanol stripper.