Journal of Applied Research in Chemical-Polymer Engineering

Abstract Research subject: The scarcity of freshwater resources and the rapid growth of population have made the development and deployment of efficient desalination technologies an undeniable necessity for securing reliable supplies of drinking and industrial water. In this regard, the present study focuses on the modeling, validation, and techno-economic assessment of two widely applied technologies, reverse osmosis (RO) and multi-effect distillation (MED), as well as their integration in standalone and hybrid configurations.
Research approach: In the modeling stage, the key components of each process, including pumps, pressure exchangers, pretreatment units, and main desalination modules, were simulated with high accuracy using established mathematical correlations. Calibration and validation were carried out with the aid of WAVE software and field data from existing industrial plants, ensuring consistency between simulation results and practical operation. Four configurations, including RO, MED, RO-MED, and MED-RO, were investigated under identical feedwater conditions (50,000 m³/day, salinity of 30,000 mg/L) and defined operational constraints. The evaluation criteria included product water quality, recovery ratio, electrical and thermal energy consumption, and specific water production cost.
Main results: The results showed that, among the four standalone and hybrid RO and MED scenarios, the standalone RO system had the lowest levelized cost of water at 0.386 USD/m³, making it the most economical option. Its cost was about 38%, 19%, and 58% lower than MED, RO-MED, and MED-RO, respectively. In contrast, the product water quality of RO was 93%, 42%, and 47% lower than these alternatives. Sensitivity analysis revealed that increasing electricity price raised the cost of standalone RO by up to 78%, while higher fuel cost increased the MED cost by 85%. Moreover, variation in the interest rate had the strongest impact on MED-RO, raising its cost by up to 40%. In addition, the RO-MED hybrid configuration proved to be an economical alternative compared with other options, while ensuring higher water quality. These findings highlight that the optimal choice of technology strongly depends on local conditions such as energy price, availability of waste heat, and required product quality.

Abstract Research subject: Chemical process design is one of the foundational pillars for the advancement of oil, gas, and petrochemical industries. The inherent complexity and multidimensional nature of chemical processes have transformed process engineering projects into highly interdisciplinary collaborations. Consequently, beyond the challenges of process design itself, the synchronization and facilitation of cooperation among diverse engineering disciplines are among the core difficulties of such projects. The involvement of process design engineers as experts in chemical engineering is essential to address these challenges through direct engagement, indirect contribution, or overarching supervision.
Research approach: This study analyzes the structural framework of chemical process engineering projects, from the ideation phase to the end of the design life cycle, focusing on the complexities of oil, gas, and petrochemical systems. The roles of process design engineers across different project phases are identified and categorized. Their decision-making frameworks are modeled using process systems engineering (PSE) methodologies. To improve interdisciplinary collaboration and data integration, an ontology-based approach is adopted to define foundational concepts and standardize relationships between project components. Additionally, to clarify decision-making mechanisms and better understand the challenges and design stages, visualization techniques are applied to map information structures and depict the decision flows and responsibilities of process design engineers.
Main results: The findings demonstrate that process design engineers play a pivotal role in the success of engineering projects at three levels: direct involvement, indirect influence, and supervisory oversight. The role of process design engineers and their decision-making in addressing challenges within oil, gas, and petrochemical projects, across all stages from concept to operation, has been analyzed through the examination of process system characteristics. The application of these analytical tools in enhancing decision-making quality and improving understanding of process systems has been presented. Emerging AI tools significantly enhance the effectiveness of engineering decisions and contribute to cost and time reduction through enabling modeling, simulation, data analysis, and optimized decision-making. Moreover, the application of these tools improves understanding of complex industrial systems and enhances performance under uncertainty.

Abstract Research subject: Chlorine and its derivatives, due to their unique properties, are used in the production of a wide range of materials in chemical, polymer, agricultural, pharmaceutical, and health industries. In fact, more than half of the marketed products worldwide are directly or indirectly related to the chlorine industry. Given the significance of these chemicals, this study aims to analyze the status of the chlorine chain in Iran’s petrochemical industry and identify its strengths, weaknesses, and investment opportunities.
Research approach: To assess the status of domestic chlorine production, the nominal and actual production capacities of petrochemical companies were collected and analyzed based on official data from the National Petrochemical Company. Using the collected statistics and data, the decrease in the operational rates of petrochemical companies was analyzed. Additionally, the share of end-use applications in the total chlorine consumption in Iran was determined and analyzed using block mass balance of the process units. To identify investment opportunities in the chlorine chain, trade data for chlorine compounds were collected and analyzed based on customs documentation, in order to identify and introduce chemicals with significant import volume or value compared to other substances.
Main results: The results showed that the majority of chlorine in Iran is used for the production of ethylene dichloride/polyvinyl chloride, with this application accounting for over 91% of chlorine consumption in the petrochemical industry. On the other hand, the high cost of replacing mercury-based chlor-alkali units with more environmentally friendly membrane technology and the inability to secure feedstock were identified as the main reasons for the decline in operational capacity for chlorine and polyvinyl chloride production in the country. Using trade statistics, methylene chloride, powder choline chloride, suspension and emulsion polyvinyl chloride, neoprene, and epichlorohydrin were identified as chemicals with high import volume and value, making the evaluation of their production projects important.

Abstract Research subject: The growing global water crisis has intensified the need to advance desalination technologies. In this regard, thermal desalination methods such as Multi-Stage Flash (MSF) and Multi-Effect Distillation (MED) are considered suitable options in regions where saline water sources are located near petrochemical and refinery plants. Their suitability stems from their capability to utilize low-grade thermal energy sources, such as flue gases from industrial processes.
Research approach: This study investigates and compares the performance of MSF and MED technologies within a flue gas heat recovery scenario. A detailed mathematical modeling framework is developed for both systems, incorporating mass and energy balance equations, heat transfer mechanisms, and economic evaluation metrics. The models are validated through comparison with experimental data obtained from various industrial units to ensure reliability and accuracy.
Main results: Simulation outcomes show that MSF, operating at a 50% recovery rate using flue gas as a heat source, has a water production cost of approximately $0.80 per cubic meter, while MED, under similar conditions, achieves a lower cost of $0.40 per cubic meter. Furthermore, the specific energy consumption is calculated to be about 15.9 kWh/m³ for MSF and 11.3 kWh/m³ for MED. Greenhouse gas emissions in the MED system are estimated to be 41% lower than in MSF at the same recovery level. From an environmental standpoint, the pollutant intensity of the concentrated brine generated by the two technologies is essentially the same. Overall, MED demonstrates superior performance over MSF in the context of flue gas heat recovery integration, due to its lower energy consumption, reduced operational cost, decreased greenhouse gas emissions, and minimized environmental impact. This study provides a comprehensive and validated numerical framework that can support simulation-based optimization of thermal desalination systems for sustainable water production.

Abstract Research subject: Real-time analysis using digital tools requires receiving instantaneous data from various points in industrial chemical processes. Time delays in measurements of process variables can affect the effective performance of different control strategies, process stability, and operational efficiency, making it impossible to analyze, extract information, and convert it into actionable decisions in real-time. The synthesis process of vinyl acetate monomer is recognized as a benchmark dynamic and nonlinear process in the chemical industry. In this process, the composition of water at the bottom of the azeotropic distillation column is one of the important variables measured by a gas chromatography (GC) analyzer, which has a significant time delay and high cost.
Research approach: Soft sensors primarily improve the real-time estimation of variables that are difficult or impossible to measure. Neural networks play an important role in the development of soft sensors due to their ability to learn nonlinear patterns and their suitable prediction speed. This study focuses on the development of a soft sensor based on a feedforward neural network model for real-time estimation of the composition of water at the bottom of an azeotropic distillation column in the vinyl acetate monomer synthesis process.
Main results: Additionally, the model was adaptively implemented under various fault conditions and accurately estimates the GC analyzer behavior instantaneously, achieving a mean squared error (MSE) of 1.1 × 10-5. Maintaining prediction accuracy in the adaptive implementation of soft sensors in the presence of various process faults demonstrates the effective adaptability of these sensors. Therefore, this study demonstrates the capability of soft sensors as an efficient and cost-effective alternative for real-time monitoring of complex chemical processes.

Abstract Research Subject: Identifying and evaluating investment opportunities across the entire network of Iranian petrochemical units is of particular importance for developing the petrochemical industry's value chain, maximizing added value, and ensuring the optimal use of oil and gas resources.

Research Approach: The purpose of this research is to create a mathematical model to identify and evaluate investment opportunities by analyzing technical and economic-financial data of process units active in the Iranian petrochemical industry. This tool can process a large volume of information in a limited time and with acceptable accuracy and provide the desired output. Process information of petrochemical complexes, including operational units, production and consumption of materials, technologies used, and prices of raw materials and products, is the main data that form the basis for preparing mathematical models. In addition to process units, environmental variables affecting the system, along with their effect on the model, are also modeled and integrated with the network of process units.

Main Results: By creating a model and performing the simulation process, the various outputs of the system include: technical and economic-financial analysis, estimation of investment costs of process units, sensitivity analysis of the network of process units to technical and economic parameters and variables such as feed and product prices, as well as the operational capacity of process units. For example, to validate the simulator outputs, the actual data of the Amirkabir Petrochemical Complex were compared with the outputs obtained from the simulator, and accordingly, the error rate of the simulator was estimated to be 3.36 percent in the estimation of the production rate of main products and 22 percent in the estimation of the production rate of by-products. Finally, based on the simulator outputs, investment opportunities in the value chain of the Iranian petrochemical industry were identified, evaluated, and validated, and on this basis, the establishment of a methanol-to-olefin (MTO) conversion unit was introduced as a valid investment opportunity in the downstream part of the methanol value chain.

Abstract Research subject: Iran is a country with high potentials for access to renewable energy sources such as solar, hydropower, wind, and biomass. Biodiesel is one of the renewable fuels that has always been proposed as a suitable and stable alternative (non-toxic, safe, and degradable) to fossil fuels.
Research approach: The experiences of different countries in the use of edible sources for biodiesel production shows that the use of edible sources has caused problems such as lack of food resources for human communities, lack of feed for livestock, and upsetting the balance in the food industry, and it can lead to a significant increase in the price of these resources. Therefore, many researchers have proposed the use of non-edible sources to address these problems. So far, very large non-edible sources for biodiesel production have been identified. In this paper, non-edible sources of biodiesel that are produced or have the potential to be produced in Iran are introduced and studied and a potential assessment study is presented for them.
Main results: In this work, the non-edible sources for biodiesel production are classified into four categories: agricultural waste, waste cooking oils, microalgae, and non-edible seeds. These sources are compared based on various parameters such as oil percentage, oil content per hectare, biodiesel production efficiency, viscosity, saponification number, and cultivation period, which according to the results, non-edible seeds, especially Nowruzak seeds, Castor, and safflower have been identified as the most rational and sustainable sources of biodiesel production in Iran. The present work also deals with the policies and incentives that the responsible institutions can apply for the prosperity of the biodiesel industry.

Abstract Research subject: Bio-hydrogen is a renewable energy source with many economic and environmental benefits as a fuel. Controlling the concentration of the substrate in the reactor has a significant effect on the amount of hydrogen production. However, bio-hydrogen production is a nonlinear process that requires the implementation of nonlinear control methods. In this paper, substrate concentration in an anaerobic bio-reactor is controlled using the feedback linearization method.
Research approach: The model employed for the simulation is a well-known model consisting of three state variables. The proposed controller is a globally linearized controller (GLC) designed based on the feedback linearization technique. In this method, the nonlinear system is precisely linearized by a transformation of the coordinate system. As a result, the linearized system can be controlled using a linear controller. In order to linearize the system, a nonlinear compensator is designed using the design model and applying the concepts of differential geometry. Proportional-integral (PI) controller is adopted as a linear controller. GLC controller performance has been compared with a nonlinear controller (NC) and a PI controller. The performance of these controllers has been studied by numerical simulation based on the integral of time-square error (ITSE).
Main results: The simulation results show that substrate concentration control can contribute to the hydrogen production. The control method applied has better set-point tracking than the other two control approaches. The ITSE performance index for the feedback linearization method is lower than the other two methods. The nonlinear feedback controller fails if the kinetic parameters are changed by 25%, but the PI method and the feedback linearization are robust against model uncertainty. An efficient controller guarantees stable bio-hydrogen production. Comparing open-loop and closed-loop simulation results shows that controlling the substrate concentration increases hydrogen production by 90%.