Optimization Of Production Systems Research Articles

Technology Focus: Completions (April 2023)

_ A few years ago, exaggerating a figure of speech, I coined the expression, “We work in a water industry; oil is just a supporting actor.” In fact, producing water is the final destination of every oil field, but obviously the objective was to highlight the importance of water management in the hydrocarbon-production process. Since the beginning, well completion and stimulation strategies have always sought to optimize water management and, extending the concept, management of all fluids produced. Selective completions in vertical and multilateral wells (combined with sand control or not) and segmentation of horizontal wells are good examples of this effort. These technologies, combined with intelligent completion, have brought very positive results. Intelligent completion in open hole, currently used in the Brazilian presalt, has allowed the reduction of the carbon footprint, so necessary nowadays. The optimal application of intelligent completion is the subject of paper SPE 209965, an excellent compendium of best practices on the topic. In recent years, driven by technological advances, good results, and the need to increase operational efficiency, we have experienced a major increase in the use of devices for flow equalization and selective fluid production [e.g., inflow control devices, autonomous inflow control devices (AICDs), and autonomous inflow control valves]. A significant reduction in water and gas production has been reported in several technical papers. In fact, this is the most published completion topic in the past 3 years, and many good articles are available. Apart from new and exciting trends, conventional solutions continue to play their important role. Basic themes long discussed continue to evolve and bring new insights. This is the case with paper SPE 204185, which deals with perforating for fracturing. Finally, paper SPE 210417 discusses the use of AICD technology in the Singue Field in Ecuador, which resulted in an increase in cumulative oil rate and a decrease in cumulative water. Recommended additional reading SPE 210205 Successful Autonomous Inflow-Control Device Application To Reduce Water Production in a Heavy Oil Greenfield by Carlos A. Pedroso, Enauta, et al. SPE 206213 Automated Well Production and Gas Lift System Diagnostics and Optimization Using an Augmented Artificial Intelligence Approach in a Complex Offshore Field by Mahmoud Elwan, Dragon Oil, et al. OTC 31483 Case Study: How the Newest Generation of Autonomous Inflow Control Devices Helps To Control Excessive Well-Water Production Within a Major Sultanate of Oman Oilfield by Salim Buwauqi, Petroleum Development Oman, et al.

Conceptual Design of an Autotrophic Multi-Strain Microalgae-Based Biorefinery: Preliminary Techno-Economic and Life Cycle Assessments

Microalgae represent a promising solution in addressing the impacts associated with the current agricultural and manufacturing practices which are causing irreparable environmental damage. Microalgae have considerable biosynthetic potential, being a rich source of lipids, proteins, and high-value compounds. Under the scope of the H2020-BBI MULTI-STR3AM project, an innovative large-scale production system of valuable commodities for the food, feed, and fragrance sectors is being developed on the basis of microalgae, reducing costs, increasing the scale of production, and boosting value chain sustainability. In this work, we aimed to create a process model that can mimic an industrial plant to estimate mass and energy balances, optimize scheduling, and calculate production costs for a large-scale plant. Three autotrophic microalgae strains (Nannochloropsis sp., Dunaliella sp. and Spirulina sp.) were considered for this assessment, as well as the use of locally sourced CO2 (flue gas). The developed process model is a useful tool for obtaining the data required for techno-economic analysis (TEA) and life cycle assessment (LCA) of industrial biorefinery-based processes. Nannochloropsis sp. was the most economic option, whereas Dunaliella sp. was the most expensive strain to produce due to its lower productivity. Preliminary environmental assessments of the climate change impact category revealed that water recirculation and the use of flue gas could lead to values of 5.6, 10.6, and 9.2 kgCO2eq·kgAFDW−1 for Nannochloropsis sp., Dunaliella sp., and Spirulina sp., respectively, with electricity and NaCl as the main contributors. The obtained data allow for the quantification of the production costs and environmental impacts of the microalgal biomass fractions produced, which will be fundamental for future comparison studies and in determining if they are higher or lower than those of the replaced products. The process model developed in this work provides a useful tool for the evaluation and optimization of large-scale microalgae production systems.

Optimization of the control paper production system of using the extremum seeking control method

Currently the pulp and paper industry is one of the fastest growing sectors of the economy in Russia and other countries. An important aspect of the further development of this industry is associated with an increase in the efficiency of paper production and the quality of finished products with an economical and rational use of raw materials, fuel and energy and other material resources. This is facilitated by the use of modern means of automation and process control. It was experimentally established that there is an extreme dependence between the ratio of the speeds of the paper pulp and the paper machine and the standard deviation of the linear density of the paper web, which determines the degree of non-uniformity of the paper gap. The use of a noise-resistant algorithm for extreme control of an inertial object based on the combination of synchronous accumulation and synchronous detection methods with subsequent search for the maximum amplitude of the second harmonic of the filtered signal, aimed at reducing the root-mean-square deviation of the mass of a square meter of paper web, is considered. It is proposed to simultaneously stabilize the mass flow rate of paper pulp and the coordinate of the fall of the paper pulp jet onto the conveyor mesh, which made it possible to increase the efficiency of paper production.

Modeling a Multi-Parameter Interaction of Geophysical Controls for Production Optimization in Gas Shale Systems.

Well bottomhole pressure optimization issue has been a significant concern for efficiently developing unconventional systems due to strong stress sensitivity. Therefore, it is of practical interest to clarify influence mechanisms involved in stress sensitivity for gas shale, which is further included in the production model to determine main controlling factors for bottomhole pressure strategy optimization for long term hydrocarbon extraction. Currently, many production models were limited in exploring stress sensitivity mechanisms but adopted common empirical equations regarding net pore stress instead. In addition, geophysical control analysis for unconventional systems optimization was mostly conducted using local sensitivity qualitative analysis, which should be validated to be reliable and applicable to fields using multi-parameter interaction influence. As a result, in this paper, an efficient workflow to rationally optimize gas well production system was provided by combining the production model, orthogonal design approach, and response surface method. To be specific, the compound flow model for shale gas reservoirs, incorporating multiple stress sensitivity mechanisms, was proposed to function as a theoretical basis for production optimization simulation. Last but not least, local sensitivity analysis was conducted to qualitatively analyze the impact of influencing factors on 20 year-production of gas wells under different bottomhole production methods. The simulation results showed that the managed pressure drawdown scheme can be adopted for reservoirs with high reservoir pressure and tight matrix properties, while the high-pressure drawdown scheme is suitable for reservoir with better fracturing effect and high external water content. Finally, based on the proposed gas flow model and orthogonal design experiments, response surface design and single factor analysis as well, an optimization mathematical model for shale gas multi-parameter interaction was established, which intuitively quantified the effects of multi-geophysical controls on EUR increase in different production durations, including matrix properties, fracture properties, and production system indicator parameters. These findings provide a more reliable reference for production system optimization based on a series of mathematical approaches to improve overall long-term recovery from shale gas reservoirs.

Extension of Climate Smart Agriculture Technology and its Policy Effects in China

Climate change has a significant impact on agriculture so it is necessary to adapt to climate change to promote agricultural transformation and sustainable development. The key to achieving such a goal is to promote the development of climate smart agriculture (CSA) against the background of climate change. The paper first uses official statistical data to evaluate the effect of climate smart agricultural technology project, and then uses the statistical method of questionnaire survey to further investigate and statistically analyze the implementation effect of climate smart agricultural technology project in the pilot area. Further, semi-structured interview method is employed to conduct more in-depth investigation and analysis on some main topics on the basis of preliminary investigation and statistical analysis. The interviews focus on the environmental and economic effects of project practice, the difficulties of smallholder farmers’ technology extension, and the farmers’ willingness to participate. The study indicates that to promote climate smart agriculture technologies is an effective way to solve agricultural problem in practice under the background of climate change, and can achieve at least three benefits. The first is to reduce production costs, which can be reduced by 10% to 15% in terms of fertilizers and pesticides alone. The second is to improve productivity, and preliminary results show that food production can be increased by more than 5%. Finally, it has carbon sequestration capabilities and can significantly reduce greenhouse gas (GHG) emissions. Therefore, the promotion of climate smart agriculture (CSA) technologies helps to solve the problem of agricultural transformation and sustainable development. To this end, it is necessary to drive the transformation and optimization of regional agricultural ecological environment and production system, and promote the innovation and extension of CSA technology. In addition, some soft conditions (e.g., talent, system and technology, etc.) and hard conditions (e.g., modern agricultural supply chain, agricultural big data and the Internet of Things, etc.), must be created, too.

Planning and Multi-Objective Optimization of Production Systems by means of Assembly Line Balancing

The ever-shorter time to market demanded by customers and an increasing number of variants lead to high-frequency product development and production system planning cycles. This article therefore presents an approach that aims at increasing efficiency in the repetitive planning of variant-specific assembly systems. Compared to the state-of-the-art manual, document-based planning prevailing in industry, formal optimization methods have the potential of considering multiple optimization criteria. This article therefore proposes an approach for assigning individual process steps to stations for a given customer cycle time while minimizing the number of stations as well as optimizing additional criteria such as increasing the production system flexibility, achieving good ergonomics or reducing the deviation within provided tolerances. The solution space of the optimization is constrained by the mathematical modeling of a limited number of stations. The operations research (OR) model developed for this purpose considers given assembly precedence graphs as an input and includes variables, parameters, objective function and constraints. Especially the latter considers the process precedence relations in terms of the Assembly Line Balancing (ALB) problem as well as a divisibility of process steps. The approach is applied to the variant-specific planning of a highly automated welding assembly in the automotive supplier industry as use case. The results show its applicability and potential in reducing planning time.

A bibliometric review of progress and trends in food factory design research

SummaryFood factory design (FFD) affects production costs, production safety and food quality. Moreover, as consumer demand, product planning, policies and legislation, raw material supplies, manufacturing philosophies and environmental consciousness change, FFD should also change. In this study, we used bibliometric analysis to examine articles on a wide range of topics related to FFD published in Web of Science from 2012 to 2022. We used collaborative network analysis, co‐occurrence analysis, cluster analysis, timeline mapping and term burst detection to achieve the following objectives: (i) summarising the annual changes in the number of publications; (ii) detecting influential countries and institutions, major research areas, context and history and representative studies at different times; and (iii) identifying frontiers and forecasting development trends in terms of factory construction and new production modes, new processes and product manufacturing technology, optimisation of production systems and processes, intelligent manufacturing, environmental protection and sustainable development. The findings of this study provide an effective means of assessing the performance of FFD research. They also help enterprises and researchers to identify important trends in FFD and provide support for decision‐making and policy formulation, and future directions for research and teaching activities.

A System Thinking Normative Approach towards Integrating the Environment into Value-Added Accounting—Paving the Way from Carbon to Environmental Neutrality

Life Cycle Assessment (LCA) is increasingly being applied in corporate accounting. Recently, especially carbon footprinting (CF) has been adopted as ‘LCA light’ in accordance with the Greenhouse Gas Protocol. According to the strategy ‘balance, reduce, substitute, compensate’, the approach is intended to provide the basis for optimization towards climate neutrality. However, two major problems arise: (1) due to the predominant focus on climate neutrality, other decisive life-cycle impact categories are often ignored, resulting in a misrecognition of potential trade-offs, and (2) LCA is not perceived as an equal method alongside cost and value-added accounting in everyday business, as it relies on a fundamentally different system understanding. In this paper, we present basic considerations for merging the business and life-cycle perspectives and introduce a novel accounting system that combines elements of traditional operational value-added accounting, process and material flow analysis as well as LCA. The method is based on an extended system thinking, a set of principles, a calculation system, and external cost factors for the impact categories climate change, stratospheric ozone depletion, air pollution, eutrophication and acidification. As a scientifically robust assessment method, the presented approach is intended to be applied in everyday operations in manufacturing companies, providing a foundation for a fundamental change in industrial thought patterns on the way to the total avoidance of negative environmental impacts (i.e., environmental neutrality). Therefore, this is validated in two application examples in the German special tools industry, proving its practicability and reproducibility as well as the suitability of specifically derived indicators for the selective optimization of production systems.

Optimization and analysis of combined heat and water production system based on a coal-fired power plant

The combined heat and water (CHW) system has been studied by many scholars since it was proposed, and a project has been implemented in Shandong, China in 2021. However, there have been few studies on optimization and comparisons of cases on the heat source side of CHW. Taking a coal-fired thermal power plant as an example, through the reasonable matching of steam flows and analysis of thermal economic parameters, the effect number and heat source steam temperature of a multi-effect distillation (MED) + multi-stage flash (MSF) combined heat and water production (CHWP) system were optimized. The optimal MED heat source temperature was found to be 70 °C, and the optimal MED and MSF effect numbers were 10 and 5, respectively. Finally, the waste heat in the power plant was utilized as much as possible throughout the year. Compared with two cases of the separate heat and water production (SHWP) system, the CHWP system easily achieved better water quality and economy under the condition of almost equal exergy efficiency, and it was easier to realize than the first case of the SHWP system.

Multistage optimization of a petroleum production system with material balance model

In this paper, we propose a mathematical formulation for the optimal management over time of an oil production network as a multistage optimization problem. The proposed model differs from common practice where the reservoir of the oil production network is approximated by decline curves or by black-box simulators. We model the reservoir as a controlled (non-linear) dynamical system by using material balance equations, under the standard assumptions that the fluids follow a black-oil model and that the reservoir has a tank-like behavior. The state of the dynamical system has five dimensions: the total volume of respectively oil, gas, and water in the reservoir; the total pore volume; and the reservoir pressure. We use a dynamic programming algorithm to numerically solve the multistage optimization problem on two specific instances of the general optimization problem where the state dimension can be reduced from dimension five to dimension one or two. More precisely, the first numerical application consists in optimizing the production of a dry gas reservoir which is subdivided in two tanks and which leads to a two-dimensional state (one dimension per tank), whereas the second numerical application tackles an oil reservoir with water injection which leads to a two-dimensional state. The two applications illustrate that our approach handles interconnected tanks (in the dry gas case) and that our approach allows optimization beyond first recovery of oil (in the oil with water injection case). We also provide numerical and theoretical comparisons with decline curves in the dry gas application.

Multi-objective optimization of subsea production systems using genetic algorithms

Multi-objective optimization of subsea production systems using genetic algorithms

Towards a domain-specific information architecture enabling the investigation and optimization of flexible production systems by utilizing artificial intelligence

The industrial domain is undergoing a major transformation, pushed forward by emerging technologies originating from research or industry. The resulting trend, better known by the term Industry 4.0, advances automation within these manufacturing companies by providing ubiquitous interconnection. This enables the integration of technologies mainly used in the Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS) or Big Data with the goal to optimize production processes or facilitate intelligent decision-making. While those processes are progressively supported by methodologies coming from the area of artificial intelligence (AI) like machine learning algorithms, sustainable and consistent storing of production data becomes increasingly important. Concluding, production systems engineering and information engineering are correlating with each other, as the respective results could be used to the advantage of the respective other discipline. In order to address these issues while developing such flexible production systems, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) has been introduced. However, practical applications are lacking as this standard is mainly described in theory, which makes it difficult to actually apply this framework. Thus, the main goal of this paper is to specify a detailed architecture description of the Information Layer to ensure the practical application of RAMI 4.0, which allows stakeholders to utilize model-based Systems Engineering (MBSE) for developing data aspects of industrial systems on the one hand and enable Information Engineering on the other hand. Supported by the concept of the Zachman Framework, the resulting architecture is applied and validated with the help of a real-world case study.

Hydrogen Production from Surplus Electricity Generated by an Autonomous Renewable System: Scenario 2040 on Grand Canary Island, Spain

The electrification of final energy uses is a key strategy to reach the desired scenario with zero greenhouse gas emissions. Many of them can be electrified with more or less difficulty, but there is a part that is difficult to electrify at a competitive cost: heavy road transport, maritime and air transport, and some industrial processes are some examples. For this reason, the possibility of using other energy vectors rather than electricity should be explored. Hydrogen can be considered a real alternative, especially considering that this transition should not be carried out immediately because, initially, the electrification would be carried out in those energy uses that are considered most feasible for this conversion. The Canary Islands’ government is making considerable efforts to promote a carbon-free energy mix, starting with renewable energy for electricity generation. Still, in the early–mid 2030s, it will be necessary to substitute heavy transport fossil fuel. For this purpose, HOMER software was used to analyze the feasibility of hydrogen production using surplus electricity produced by the future electricity system. The results of previous research on the optimal generation MIX for Grand Canary Island, based exclusively on renewable sources, were used. This previous research considers three possible scenarios where electricity surplus is in the range of 2.3–4.9 TWh/year. Several optimized scenarios using demand-side management techniques were also studied. Therefore, based on the electricity surpluses of these scenarios, the optimization of hydrogen production and storage systems was carried out, always covering at least the final hydrogen demand of the island. As a result, it is concluded that it would be possible to produce 3.5 × 104 to 7.68 × 104 t of H2/year. In these scenarios, 3.15 × 105 to 6.91 × 105 t of water per year would be required, and there could be a potential production of 2.8 × 105 to 6.14 × 105 t of O2 per year.

Minimizing mission cost for production system with unreliable storage

Considerable research efforts have recently been devoted to the reliability analysis and optimization of production systems with product storage. The existing models, however, failed to address a crucial design factor of cost. This paper makes contributions by modeling the expected mission cost (EMC) of an imperfect production system with storage subject to failures during uploading and downloading processes. To perform a successful mission, the system must meet a certain demand during a specified mission time. The EMC modeled encompasses operation and standby cost of the production unit, downloading and uploading cost of the storage, cost of losses associated with the failure of the production unit and storage, as well as penalty cost associated with the mission failure. The optimal EMC problem is formulated and solved, which determines the scheduled storage uploading time to minimize the EMC. A detailed case study of a coal feeding system is conducted to demonstrate the proposed probabilistic evaluation method and influences of different model parameters on the mission success probability and EMC as well as on the optimization solutions. The advantage of using the production storage is also illustrated through a comparison to a dual-production unit system without storage.

Optimization of China's pig production system to reduce environmental impacts based on a data envelopment and life cycle analysis model

Pig production system has significant impacts on global climate and environment; the greenhouse gas (GHG) annual emission from China's pig production system accounts for more than 4% of which from world's animal husbandry. This study applied DEA and LCA methods with statistics of China's pig production, compared the environmental impact in each stage before and after optimization, aimed to calculate the resource input and energy consumption in pig production system, and realize lower investment and higher efficiency. The results showed that the optimized method could reduce 55.69 MJ energy consumption from each pig; the environmental impact potential of global warming, environmental acidification and eutrophication decreased by 1.56%, 0.6% and 0. 072%. Considering the Chinese pig breeding market in 2018 as an example, with a total of 693.824 million pigs sold, the optimized GHG emission reduction would be equivalent to the GHG emitted by producing 1.92812 × 105 vehicles.

Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass

Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.

Optimization of petroleum production system using Nodal Analysis Program

This research attempted to optimize petroleum production system of well X in Field Y in Niger Delta region of Nigeria located in Gulf of Guinea by adopting Nodal analysis technique. A non-commercial software known as Nodal Analysis Program was used for the analysis. The dataset available from offset well were used as the input parameters to the software for the selection of the most economical production string for the new well. The production system has two adjustable components: vertical tubing and nearly horizontal flowline. The flowline inclination is -3.0 degree to the horizontal. The productivity index of the well was obtained in order to know the deliverability of the well. Several combinations of the tubing and flowline have been used in the analysis of the production system. The optimal configuration of the production system components is selected by the maximum operating flow rate of 1118 stb/day. The stable operational region is determined with the assumption that the system will be stable above the flow rate corresponding to the minimum on the outflow performance relation (OPR) curve. The introduction of the gas lift into the optimal system configuration increased the operating oil rate from 1118 stb/day to production rate of approximately 1287 stb/day, but the operating oil rate decreased with higher gas injection rate to 1115 stb/day. The optimal gas injection rate is selected by highest operating oil rate. The fluctuations in the oil price did not change the selection of the optimal configuration and gas injection rate. The investigation of the flow regime in the system before and after gas lift has revealed that the effect of gas injection on the flow regime is minor, probably due to low injection rate. Disperse flow was the flow regimes investigated and established for vertical flow (tubing) before and after gas injection. While on the other hand, elongated bubble was established to be the flow regime in flowline before gas injection and slug flow after gas injection in the flowline.

Special Issue on Smart Factory

A Fourth Industrial Revolution has been proposed, and various research and development activities geared toward the realization of smart factories have become extremely active. In smart factories developed individually within companies, the direction of the development activities is changing drastically. Regarding common technologies, research activities and feasible case study activities that are collaborations among industry, academia, and government and that show consideration and awareness of ecosystems are becoming active. It has been five years since the IJAT last published a special issue on smart manufacturing. Smart factory technologies are becoming more and more important in the industrial world, not as a temporary boom, but as a steady development. Research on smart factory technologies is progressing, as they are considered to be important technologies in the industrial world, along with Digital Transformation (DX) technologies, among others. This special issue addresses the latest in advanced research on smart factories. We have received many submissions from researchers at universities, public research institutes, and companies, and we are pleased to publish eight papers. The latest research being published mainly concerns the following five topics: - Smart and digital twinning of machine tools - Smart and digital twinning of production systems - Optimization of production systems using scheduling algorithms - Optimization of production systems including worker operations - Optimization of energy consumption for production systems The results of these studies will serve as a reference for further smart factory technologies in the future. We deeply appreciate the careful work of all the authors, and we thank the reviewers for their incisive efforts. Without these contributions, this special issue could not have been created. We also hope that this special issue will trigger leading to further advances in smart factories.

Methods for the Cost-Effective Production of Bacteria-Derived Double-Stranded RNA for in vitro Knockdown Studies.

RNA interference (RNAi) is a highly conserved pathway for the post-transcriptional regulation of gene expression. It has become a crucial tool in life science research, with promising potential for pest-management applications. To induce an RNAi response, long double-stranded RNA (dsRNA) sequences specific to the target gene must be delivered to the cells. This dsRNA substrate is then processed to small RNA (sRNA) fragments that direct the silencing response. A major obstacle to applying this technique is the need to produce sufficiently large amounts of dsRNA in a very cost-effective manner. To overcome this issue, much attention has been given to the development and optimization of biological production systems. One such system is the E. coli HT115 strain transformed with the L4440 vector. While its effectiveness at inducing knockdowns in animals through feeding of the bacteria has been demonstrated, there is only limited knowledge on the applicability of bacteria-derived dsRNA for in vitro experiments. In this paper, we describe and compare methods for the economical (43.2 €/mg) and large-scale (mg range) production of high-quality dsRNA from the HT115 bacterial system. We transformed the bacteria with constructs targeting the Helicoverpa-specific gene Dicer2 and, as a non-endogenous control, the Green Fluorescent Protein gene (GFP). First, we compared the total RNA extraction yields of four cell-lysis treatments: heating, lysozyme digestion, sonication, and a control protocol. Second, we assessed the quality and purity of these extracted dsRNAs. Third, we compared methods for the further purification of dsRNAs from crude RNA extracts. Finally, we demonstrated the efficiency of the produced dsRNAs at inducing knockdowns in a lepidopteran cell line. The insights and results from this paper will empower researchers to conduct otherwise prohibitively expensive knockdown studies, and greatly reduce the production times of routinely or large-scale utilized dsRNA substrates.

Optimization of a renewable hydrogen production system from food waste: A combination of anaerobic digestion and biogas reforming

In this study, hydrogen production using food waste was optimized by investigating the effect of agitator types in anaerobic digestion reactors and catalysts for biogas reforming. The applied agitators were pitched blade and hydrofoil, and their effect on homogeneity was estimated using computational fluid dynamics. Reactors with different agitators were operated for 60 days for biogas production. Increased biogas production was observed in the reactor equipped with a hydrofoil agitator owing to its high homogeneity. In addition, Ni-CeZrO2 catalysts promoted with La2O3, CaO, or MgO were investigated for stable hydrogen production during the biogas reforming reaction using simulated gas based on biogas from the anaerobic digestion equipped the hydrofoil. Among the promoted catalysts, the MgO-promoted Ni-CeZrO2 catalyst displayed the best results for hydrogen production without significant deactivation. The stable catalytic performance of the MgO-promoted catalyst resulted from the close interaction between Ni and MgO, and its high oxygen storage capacity. Thus, 1216 L hydrogen and 646 L carbon monoxide were produced per kilogram volatile solid via the hydrogen production system that included anaerobic digestion and biogas reforming.