Increase In Demand Research Articles

Double-Layer Optimization and Benefit Analysis of Shared Energy Storage Investment Considering Life-Cycle Carbon Emission

As a crucial path to promote the sustainable development of power systems, shared energy storage (SES) is receiving more and more attention. The SES generates carbon emissions during its manufacturing, usage, and recycling process, the neglect of which will introduce a certain extent of errors to the investment of SES, especially in the context of the large-scale integration of renewable energy and dramatic increase in demand for SES capacity. To enhance the accuracy of SES investment, we propose a double-layer optimization model to compute the optimal configuration of a shared energy storage station (SESS) considering its life-cycle carbon emission. First, the service mode, settlement method, profit mechanism, and application scenarios of SESS are introduced. Second, the life-cycle assessment approach is used to calculate the life-cycle carbon emission of SESS, and the uncertainty of supply and demand is considered. Then, a double-layer optimization model that considers the economic operation of multi-microgrid systems and the optimal allocation of SESS is established. The lower-layer model’s Karush–Kuhn–Tucher (KKT) condition is derived to convert the double-layer model into a single-layer one. Finally, a combined heat and power (CHP) three-microgrid system is used to demonstrate the validity of our proposed model, and the economy of SESS investment is analyzed from multiple perspectives. The results show that considering the life-cycle carbon emission of SESS can provide more accurate guidance for investing in and measuring the carbon emission and reduction for SESS.

Scope of Rain-fed Integrated Farming System in Dry Farming Condition: A Review

In India, nearly 76 million hectares of land is under rain fed cultivation and therefore, rain fed agriculture will continue to play an important role in the Indian economy. The discouraging results of conventional cropping systems in advancing the productivity of small farms were the driving force in the development of farming systems research. It views the whole farm as a system with the integration of crops, animals, poultry, soils, labor and other all available inputs and environmental influences wherein the farm family attempts to produce outputs within the limitations of its capability and resources and the socio-cultural setting. IFS seem to be the possible solution to the continuous increase of demand for food and nutrition and income stability particularly for small and marginal farmers with little resources. This new concept of IFS will help in productivity enhancement, employment generation, gave more income and nutritional security both for human and livestock. IFS different components are complementary relate with each other and one component become source of food for other components. This literature on scope of rain-fed integrated farming system in dry farming condition is reviewed and presented below.

Gwydion: Efficient auto-scaling for complex containerized applications in Kubernetes through Reinforcement Learning

Containers have reshaped application deployment and life-cycle management in recent cloud platforms. The paradigm shift from large monolithic applications to complex graphs of loosely-coupled microservices aims to increase deployment flexibility and operational efficiency. However, efficient allocation and scaling of microservice applications is challenging due to their intricate inter-dependencies. Existing works do not consider microservice dependencies, which could lead to the application’s performance degradation when service demand increases. As dependencies increase, communication between microservices becomes more complex and frequent, leading to slower response times and higher resource consumption, especially during high demand. In addition, performance issues in one microservice can also trigger a ripple effect across dependent services, exacerbating the performance degradation across the entire application. This paper studies the impact of microservice inter-dependencies in auto-scaling by proposing Gwydion, a novel framework that enables different auto-scaling goals through Reinforcement Learning (RL) algorithms. Gwydion has been developed based on the OpenAI Gym library and customized for the popular Kubernetes (K8s) platform to bridge the gap between RL and auto-scaling research by training RL algorithms on real cloud environments for two opposing reward strategies: cost-aware and latency-aware. Gwydion focuses on improving resource usage and reducing the application’s response time by considering microservice inter-dependencies when scaling horizontally. Experiments with microservice benchmark applications, such as Redis Cluster (RC) and Online Boutique (OB), show that RL agents can reduce deployment costs and the application’s response time compared to default scaling mechanisms, achieving up to 50% lower latency while avoiding performance degradation. For RC, cost-aware algorithms can reduce the number of deployed pods (2 to 4), resulting in slightly higher latency (300μs to 6 ms) but lower resource consumption. For OB, all RL algorithms exhibit a notable response time improvement by considering all microservices in the observation space, enabling the sequential triggering of actions across different deployments. This leads to nearly 30% cost savings while maintaining consistently lower latency throughout the experiment. Gwydion aims to advance auto-scaling research in a rapidly evolving dynamic cloud environment.

Navigating job demands and resources in policing: the role of self-efficacy in work burnout and engagement

PurposeThis study examined the effects of job demands and resources specific to policing on work burnout and engagement, including self-efficacy’s role in expanding the job demands-resources model (JD-R model).Design/methodology/approachThe authors used survey data from 1,591 South Korean police by nationwide sampling officers in 2023. A moderation analysis was used to examine the relationships between each variable.FindingsSelf-efficacy moderates the effects of job demands and job resources on work burnout but not on work engagement. The high self-efficacy group generally exhibits lower levels of work burnout than the low self-efficacy group. The high self-efficacy group is associated with higher levels of work burnout as job demands increase and lower levels of work burnout as job resources increase. The low self-efficacy group experiences higher levels of work burnout independent of job demands and job resources than the high self-efficacy group. In the low self-efficacy group, the reduction in job burnout is very gentle as job resources increase. Also, the increase in job burnout is steep as job demands increase.Originality/valueJob resources are generally considered helpful in reducing work burnout. However, the current study highlights the necessity for delicate interventions considering self-efficacy levels.

Diversity and ecotourism on multipurpose marine protected areas

Beyond its ecological benefits, there is a growing call for the establishment of marine protected areas (MPAs) for varied economic activities. Due to the decline in the wild catch, there is an increase in demand for farmed fish and a call for the development of aquaculture production in marine protected areas. In addition, the diversity in marine protected areas is suitable for the introduction of a tourism industry. Therefore, we need to design a clear scientific management system that balances the main goal of marine protected areas with the economic benefits, of aquaculture and tourism activities. This paper aims to determine the optimal size of the marine protected areas, the aquaculture size in the protected areas, and the level of tourism activities that maximize the overall benefit of the stakeholders. This is vital for the co-existence and sustainability of different sectors such as aquaculture production, tourism activities, and open-access fisheries.

Using Artificial Neural Networks to Evaluate the Capacity and Cost of Multi-Fiber Optical Backbone Networks

A possible solution to address the enormous increase in traffic demands faced by network operators is to rely on multi-fiber optical backbone networks. These networks use multiple optical fibers between adjacent nodes, and, when properly designed, they are capable of handling petabits of data per second (Pbit/s). In this paper, an artificial neural network (ANN) model is investigated to estimate both the capacity and cost of a multi-fiber optical network. Furthermore, a fiber assignment algorithm is also proposed to complement the network design, enabling the generation of datasets for training and testing of the developed ANN model. The model consists of three layers, including one hidden layer with 50 hidden units. The results show that for a large network, such as one with 100 nodes, the model can estimate performance metrics with an average relative error of less than 0.4% for capacity and 4% for cost, while achieving a computation time nearly 800 times faster than the heuristic approach used in network simulation. Additionally, the network capacity is around 5 Pbit/s.

Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization

The increase in global power demand has caused most of today's power networks to become overloaded especially in Sub-Saharan Africa. The increased load demand can be met through expansion of existing generation and transmission system. However, construction of new power infrastructure is limited by financing and technical constraints. Thus, power networks have been left to operate at overload conditions with high power losses and many power quality (PQ) problems. Flexible AC Transmission System (FACTS) devices can improve the power transfer capability of the existing transmission networks without the need of constructing new power infrastructure. In this paper, a multi-objective function comprising of minimization of power loss (PL), voltage deviation (VD) and operational cost (OC) was formulated and solved using a novel algorithm. A novel Genetic Algorithm-Improved Particle Swarm Optimization (GA-IPSO) technique is proposed in this paper for optimization of size and location of FACTS devices. Static Synchronous Compensator (STATCOM), Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC) are the three FACTS devices considered. The proposed technique was validated using IEEE-33 Bus Test System, which is a popular benchmark Radial Distribution System (RDS). The three FACTS devices were optimized separately and also in a combined manner. Under the separate optimization, the size and location of individual FACTS devices were optimized. For combined optimization, the sizes and locations of more than one device were optimized in the same test system. For separate optimization, UPFC produced the best results by reducing the active power losses by 38.44 % and OC from $1.59×105 to $ 1.15×105. Under the combined optimization, combination of TCSC, STATCOM and UPFC gave better results by achieving active power loss reduction of 56.09 % and reducing OC from $1.59×105 to $ 1.03×105. Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.

Analysis of the Forward and Backward Linkages of Agriculture, Forestry and Fisheries Sectors in Central Java Province

This study aims to analyze the linkages forward and backward sectors of Agriculture, Forestry and fisheries in the economic development of Central Java province. This study uses data Input Output table of Central Java in 2016. The method of data analysis used in this study is Input-Output Analysis. The results showed that the subsector in the Agriculture, Forestry and fisheries sector that has the highest value of direct and indirect backward linkage is the livestock subsector. This means that the livestock subsector means that each increase in final demand in the livestock subsector by 1 unit will increase the output of other economic sectors that are used directly or indirectly as inputs to the livestock subsector by 1.5330 units. On the other hand, the value of direct and indirect future linkages in the agricultural, forestry and fisheries sectors is the highest, namely the food crop subsector with a value of 1.3903. This means that every increase in output in all sectors of the economy by 1 unit will increase the output of the food crop subsector by 1.3903 which is used directly as input for all sectors of the economy. Keywords: Backward Linkage, Forward Linkage, Economic Development, Agriculture, Forestry And Fisheries Sectors

Decentralized Energy Systems and the Circular Economy: Business Models for Sustainable Energy Transitions

To address current rising northern environmental concerns, and a remarkable increase in energy demand, this research aims to investigate the feasibility of implementing both decentralized energy systems and circular economy approach for successful sustainable energy transformations. As a general goal of this research, the aim is to compare different business models of agent-based decentralized energy production according to circular economy principles of efficiency, waste reduction, and secure sources of supply chains. It adopted both quantitative treatment of energy data pertaining to decentralized systems in European and Asian countries and qualitative findings generated from industry case studies. The study shows that business models include energy as a service and community power which plays an important role in energy saving resulting in operational efficiency after the application of the analyzed models leading to a decrease in energy losses ranging between 1530%. The major contribution of this study is to provide an integrative framework that not only emphasises the possibility of decentralised scenarios but also reveals how they can contribute to circular economy aims such as resource circulation and low carbon. Based on the findings of this study, there are insights drawn that speaker to policymakers, business strategists, and environmental organizations as these details a coherent explanation of models of sustainable energy as well as coming up with practical models that can be adopted in largescale manner. The approach of this research within its quantitative and practical methodologies provides valuable insights to inform the analysis of decentralised energy as it contributes to international agenda towards sustainability.

Projection of Changes in Stream Water Use Due to Climate Change

This study investigates the impact of rising temperatures on residential water use (RWU) in Seoul from 2015 to 2021, addressing the challenges of urban water sustainability under climate change. Using advanced models—convolutional neural networks (CNNs), long short-term memory (LSTM) Networks, eXtreme Gradient Boosting (XGBoost), and Bayesian Neural Networks (BNNs)—we examined RWU prediction accuracy and incorporated a method to quantify prediction uncertainties. As a result, the BNN model emerged as a robust alternative, demonstrating competitive predictive accuracy and the capability to account for uncertainties in predictions. Recent studies highlight a strong correlation between rising temperatures and increased RWU, especially during summer, with tropical nights (with temperatures above 25 °C) becoming more common; Seoul experienced a record 26 consecutive tropical nights in July 2024, underscoring a trend toward higher RWU. To capture these dynamics, we employed Shared Socioeconomic Pathway (SSP) scenarios and downscaled the KACE-1-0-G Global Climate Model (GCM) for Seoul, projecting a progressive increase in RWU: 0.49% in the F1 period (2011–2040), 1.53% in F2 (2041–2070), and 2.95% in F3 (2071–2100), with significant rises in maximum RWU across these intervals. Our findings highlight an urgent need for proactive measures to secure water resources in response to the anticipated increase in urban water demand driven by climate change.

Supply Chain Analysis Based on Community Detection of Multi-Layer Weighted Networks

As the economic environment becomes more complex, improving supply chain resilience is critical for the effective operation and long-term sustainability of businesses. Real-world supply chains, which consist of various components such as goods, warehouses, and plants, often feature intricate network structures that pose challenges for resilience analysis. This paper addresses these challenges by proposing a framework for studying supply chains using multi-layer network community detection. The complex multi-mode supply chain network is transformed into single-mode, multi-layer weighted networks. A multi-layer weighted community detection method is proposed for identifying local clusters within these networks, revealing interconnected groups that highlight flexibility and redundancy in production capabilities across different plants and goods. An empirical study utilizing real data demonstrates that this clustering method effectively detects indirect capacity links between plants and goods. The insights derived from this method are useful for strategic capacity management, allowing businesses to respond more effectively to supply shortages and unexpected increases in demand.

Obstructive shock and cardiac arrest due to diaphragmatic hernia after esophageal surgery: a case report.

We report the exceedingly rare case of diaphragmatic hernia after esophageal surgery resulting in obstructive shock and cardiac arrest. An 82-year-old man, who had undergone a robotic-assisted thoracoscopic esophagectomy and gastric tube reconstruction via a subcutaneously route with three-field lymphadenectomy for esophagogastric junction cancer at another hospital 3months prior, complaining of persistent epigastric pain and nausea. Computed tomography revealed that the proximal jejunum had herniated through the esophageal hiatus into the left thoracic cavity, with dilation of the subcutaneous gastric tube and duodenum. He was urgently admitted, and a nasogastric tube was inserted. His respiratory and circulatory parameters were normal upon admission, however, nine hours after admission, there was a rapid increase in oxygen demand, and he subsequently developed shock. His blood pressure was 106/65 mmHg, pulse rate of 150bpm, respiratory rate of 30/min with an O2 saturation of 97% on High-flow nasal cannula FiO2:0.4, cyanosis and peripheral coldness appeared. Chest X-ray showed a severe mediastinal shift to the right, suggesting obstructive shock due to intestinal hernia into the thoracic cavity. Emergency surgery was planned, but shortly after endotracheal intubation, the patient experienced cardiac arrest. It was found that approximately 220 cm of small intestine had herniated into the thoracic cavity through the esophageal hiatus, and it was being strangulated by the diaphragmatic crura. A portion of the diaphragmatic crura was incised to manually reduce the herniated small intestine back into the abdominal cavity. The strangulated intestine was congested, but improvement in coloration was observed and it had not become necrotic. The procedure finished with closure of the esophageal hiatus. Intensive care was continued, but he died on postoperative day 29 because of complications including perforation of the descending colon and aspiration pneumonia. Rapid progression of small intestine hernia into the thoracic cavity, leading to obstructive shock, was suspected. While this case was rare, early recognition of the condition and prompt reduction could have potentially led to life-saving outcomes.

Ecological compensation based on multiscale ecosystem carbon sequestration service flow

Scientific assessments of the supply, demand and flow of ecosystem services and the formulation of ecological compensation policies are important for the promotion of sustainable regional development. Based on the supply–demand ratio model, breakpoint model, field strength model, geographic information system spatial analysis and statistical methods, we assessed the supply, demand and supply–demand relationships of carbon sequestration services on the Loess Plateau for 2000, 2010 and 2020. We also analyzed the interregional flow of carbon sequestration services at multiple scales and accounted for horizontal ecological compensation. The results revealed that from 2000 to 2020, both the supply and demand of carbon sequestration services increased, with a greater increase in demand. The high-supply areas were mostly in the central and northwestern parts of the Loess Plateau, and the high-demand areas were mostly in areas other than the central part. The supply–demand ratio for carbon sequestration services declined, with a large increase in the number of deficit counties and surplus counties, mostly in the central and western parts of the study area. The flow rates of carbon sequestration services within the Loess Plateau increased. The intercounty flow rates within the same city were the highest but decreased. Decreasing intercity flow rates appeared within the same province although they initially increased. Finally, interprovincial flow rates were the lowest, but they were increasing. The outflow of carbon sequestration services from the Loess Plateau to external regions was greater than the inflow from external regions, but the outflow decreased in 2020 compared with 2010, while the inflow from external regions increased. Most cities actually received or paid less ecological compensation than that accounted for based on the basis of flow rates after considering willingness to pay and ability to pay. Moreover, the number and total compensation of cities receiving ecological compensation were greater than the number and total compensation of payers in the 44 cities within the Loess Plateau. This study provides a theoretical basis for not only understanding the spatial transfer patterns of ecosystem services at multiple scales but also formulating ecological compensation policies, thereby promoting the realization of regional sustainable development.

Pengaruh Konsentrasi Natrium Hidroksida dan Waktu Pemasakan Terhadap Penurunan Kadar Lignin Menggunakan Kulit Edamame (Glycin Max (L) Merill) dengan Proses Soda

The increase in demand for pulp and kertas raises awareness of the problem of kayu hutan as the primary raw material for pulp and kertas production. As a result, an alternative to kayu is required. One non-kayu tanaman that has the potential to be used as an alternative raw material in pulp production is edamame kulit (Glycin max (L) merrill), which is slowly brewed or baked such that it can cause environmental problems. Kulit edamame has a relatively high selulosa content of 2.3% to 13.1%. This study aims to understand the effects of pemasakan and the concentration of natrium hydroxide on the lignin pulp from kulit edamame using the soda process. The best method for this study is to use NaOH concentrations of 0.7%, 0.9%, and 1.78%, 1.82, and 1.86 percent at pemasakan times (100, 115, 125, 135, 145 menit) has lignin tertinggi kadar of 2,0376% and lignin terendah kadar of 0,9000%. The purpose of this study is to determine the pulp quality that is obtained from the soda process using NaOH concentration and the best combination of NaOH concentration and pemasakan time in the production of pulp from Kulit Edamame. The results of the study show that as the concentration of NaOH increases, the amount of lignin will decrease. This is because increasing the concentration of pemasakan's lignin can increase the amount of delignification and the amount of lignin that degrades will result in better pulp. And the longer the pemasakan time, the longer the lignin kadar will flow, resulting in a pulp that is more putih and has better quality.

Kinetic Control over Social and Narcissistic Self-Sorting from Multicomponent Mixtures in Seed-Initiated Supramolecular Polymerization by Fine-Tuning of Steric Effects.

Supramolecular polymers offer an intriguing possibility to transfer molecular properties from the nano- to the mesoscale. Towards this achievement, seed-initiated supramolecular polymerization has emerged as a powerful tool, as it prevents unlimited growth and enables size control of the assembly outcome. However, the potential application of the seeding method in the context of complex supramolecular systems is hitherto unclear. Herein we demonstrate that minute differences in molecular design in direct proximity to intermolecular recognition sites govern the molecular packing and in turn dictate the efficacy of seeded polymerization processes. We introduce a stepwise increase in steric demand in the central amino acid residue of a diamide system, which gradually increases the rotational displacement within the aggregated state. This fine-tuning of the molecular packing directly affects the propensity of the different aggregates to act as seeds for the other supramolecular synthons. In turn this allows us to selectively target specific trapped monomer states in binary mixtures for social or narcissistic seeded polymerization.

Kinetic Control over Social and Narcissistic Self‐Sorting from Multicomponent Mixtures in Seed‐Initiated Supramolecular Polymerization by Fine‐Tuning of Steric Effects

AbstractSupramolecular polymers offer an intriguing possibility to transfer molecular properties from the nano‐ to the mesoscale. Towards this achievement, seed‐initiated supramolecular polymerization has emerged as a powerful tool, as it prevents unlimited growth and enables size control of the assembly outcome. However, the potential application of the seeding method in the context of complex supramolecular systems is hitherto unclear. Herein we demonstrate that minute differences in molecular design in direct proximity to intermolecular recognition sites govern the molecular packing and in turn dictate the efficacy of seeded polymerization processes. We introduce a stepwise increase in steric demand in the central amino acid residue of a diamide system, which gradually increases the rotational displacement within the aggregated state. This fine‐tuning of the molecular packing directly affects the propensity of the different aggregates to act as seeds for the other supramolecular synthons. In turn this allows us to selectively target specific trapped monomer states in binary mixtures for social or narcissistic seeded polymerization.

Analysis of future water demands in Al-Madinah (1990–2030) based on the modified IPAT model and sheared socio-economic pathways scenarios

Arid regions such as Saudi Arabia are facing water scarcity and availability issues and experiencing growing pressure by rapid water consumption. Determining the main driving forces contributing to rising water demands and future water demand prediction are considered the cornerstone for developing a good sustainable management plan. Al-Madinah city was chosen as a case study. In this study, the population, affluence, and technology (IPAT) model has been modified to apply in the water sector to analyse water needs in Al-Madinah from 1990 to 2020 and examines factors including population, GDP-per capita, agricultural lands/GDP and built-up area/agricultural lands. In addition, Sheared Socio-economic Scenarios (SSPs) have been developed to predict water demands in Al-Madinah from 2020 to 2030. The results confirm that population was the most important in explaining water consumption trends. Moreover, water demand under all IPCC_ SSP scenarios is expected to increase by between 17 to 28%. The scenarios of SSP3 and SSP4 are projected to experience an increase in water demands by an average of 25% and 26%, respectively. In contrast, the water demand is forecasted to lower under the SSP1 and SSP5 by around 20% and 17%, respectively. This evaluation could highly reinforce and improve sustainable water resource management strategies, which have recently become increasingly essential to face growing water challenges and demands.

Energy Solutions for Decarbonization of Industrial Heat Processes

The global rise in population and advancement in civilization have led to a substantial increase in energy demand, particularly in the industrial sector. This sector accounts for a considerable proportion of total energy consumption, with approximately three-quarters of its energy consumption being used for heat processes. To meet the Paris Agreement goals, countries are aligning policies with international agreements, and companies are setting net-zero targets. Upstream emissions of the Scope 3 category refer to activities in the company’s supply chain, being crucial for achieving its net-zero ambitions. This study analyzes heating solutions for the supply chain of certain globally operating companies, contributing to their 2030 carbon-neutral ambition. The objective is to identify current and emerging heating solutions from carbon dioxide equivalent (CO2e) impact, economic, and technical perspectives, considering regional aspects. The methodology includes qualitative and quantitative surveys to identify heating solutions and gather regional CO2e emission factors and energy prices. Calculations estimate the CO2e emissions and energy costs for each technology or fuel, considering each solution’s efficiency. The study focuses on Europe, the United States, Brazil, China, and Saudi Arabia, regions or countries representative of companies’ global supply chain setups. Results indicate that heat pumps are the optimal solution for low temperatures, while biomass is the second most prevalent solution, except in Saudi Arabia where natural gas is more feasible. For medium and high temperatures, natural gas is viable in the short term for Saudi Arabia and China, while biomass and electrification are beneficial for other regions. The proportion of electricity in the energy mix is expected to increase, but achieving decarbonization targets requires cleaner energy mixes or competitive Power Purchase Agreement (PPA) projects. Brazil, with its high proportion of renewable energy sources, offers favorable conditions for using green electricity to reduce emissions. The utilization of biomethane is promising if costs and incentives align with those in the EU. Although not the objective of this study, a comprehensive analysis of CAPEX and lifecycle costs associated with equipment is necessary when migrating technologies. Policies and economic incentives can also make these solutions more or less favorable.

Targeted Study of the Effect of Yeast Strain on Volatile Compounds Produced in Sorghum Beer.

An increase in the consumer demand and the availability of gluten-free products has led to several brewers investigating brewing with grains other than barley. The primary grain of choice has been sorghum. These new gluten-free beers have a unique flavor and aroma, which previous research has shown is the result of differences in concentration for key chemical compounds, including ethyl butyrate, butyl acetate, isoamyl acetate, ethyl caproate, hexyl acetate, 1-octanol, nonanal, ethyl octanoate, and ethyl decanoate. This study focused looked at the influence different strains of yeast had on the concentration of these key compounds. Beer was brewed using either barley or sorghum malt extract. The concentrations of these key volatile compounds were determined using Solid Phase Microextraction (SPME) with Gas Chromatography and Mass Spectral (GC-MS) detection. Overall, it was found that the concentrations of these compounds were statistically different in the beers brewed from these two grain types. However, the yeast strain had no significant impact on the concentrations.

Design of a Bubbling Fluidized Bed Reactor for Bio-oil Production from Sawdust

Due to the ever increase in energy demand, it becomes imperative to design appropriate reactor for the conversion of available raw materials to energy source such as bio oil. A fluidized bed reactor is one the most promising reactor for the conversion of biomass to bio-oil via pyrolysis. This work take into consideration the design of fluidized bed reactor and its subsidiaries (cyclone, filter, condenser and feed hopper) to be used for bio-oil production using sawdust as the feedstock. The operating conditions were temperature between 400-6000C, pressure 9.9atm and flow rate 1.86m/s. From the design calculations, it was found that the volume of the reactor, area, height, diameter and minimum fluidized velocity were 15.0796mm3, 5026mm2, 300mm, 80mm and 1.86m/s respectively. The material for construction for the reactor is stainless steel because of its high thermal conductivity and resistance to corrosion.