The activity in R&D is very diverse. The results are visible every day, although they need important periods of time in order to bear fruit. Success in this field is the result of a constant effort. Clearly, the maturing period is higher than political mandates, and probably this is the main difficulty in achieving a political consensus.
The Spanish society is not aware that their standard of living is linked to the rate of advancement of science and technology in our country. Therefore, our leaders do not feel any political pressure to avoid the lack of public resources dedicated to R&D. It is as if almost no one is interested in changing this situation. The famous cry of the Spanish writer Unamuno, ‘Let them invent’ is seen as the reflection of suicidal thought of lots of Spanish people.
Here, there are some data of 2014, last released by the Spanish Statistics Institute. The Spanish R&D resources rise to 12.821 million euros, 1,5% less than the previous year. This represents 1,23% of GDP and with this data, we go back to the situation of 2003, a trend that began in 2010. Our position is below the EU average: 2,02%. It is too below Portugal, 1,34%, and far from Germany and the Nordic countries, whose spending is approximately 3% of their GDP. Unlike in Spain, the EU average continued to rise in the year of the Great Recession. The situation is even worse if we compare ourselves with world leaders; South Korea spent 4,04% in 2012, and very near Japan and USA.
Data become more unfavorable if we delve into 2014, because the Spanish Government reduced the resources devoted to R&D by 1,1%, and enterprises 1,8%.
The public sector data are real. The companies’ percentages of the annual survey are made by the Spanish Statistics Institute, following the methodology of the Frascati Manual. However, it could be possible that many companies, for tax reasons or prestige, declare as R&D maintenance expenses and others. There are more circumstances that may affect survey numbers, such as capacity expansions.
There are four regions that increased their R&D spending in 2014: La Rioja, Murcia, Galicia and Valencia. The rest reduced them. In relative value in % of GDP, there is a big dispersion: The Basque Country with 2,03%, Navarra with 1,75%, Madrid with 1,68%, Catalonia with 1,47%, above the national average. A great distance followed by other regions, like Andalucía with 1,03% or Baleares with 0,32%. These percentages show the lack of interest from both administrations, central and regional, in encouraging basic engine of economic and social progress.
Three steps to reduce waste, emissions and the use of resources
“Green Manufacturing” can be defined in many ways, but in this post and the following ones, we will focus on the “greening” of manufacturing, this is, reducing pollution and waste by minimizing the use of natural resources, recycling and reusing waste and reducing emissions.
A growing number of businesses are finding those investments on reducing waste, pollution and the use of natural resources, along with recycling and reusing what was formerly considered waste, yields benefits not only in terms of an improved bottom line, but also in terms of employee motivation, morale, and public relations.
There are individual and collective initiatives with private, public or even both types of funding. One of them, in which I am involved, is the demonstrative REEMAIN project. In this project, supported by the EU 7th Framework Program, we promote innovative strategies on the use of resources (energy and materials) at the factory, including the optimization of the production-process-product, a seamless integration of renewable energy systems and the recovery of wasted energy. This project has demonstration activities in three factories: a biscuit factory, a foundry and a textile factory specialised in producing denim.
Our “recipe” is based on three consecutive steps: first Reduce, then Recover and finally, Replace.
It is also possible to make use of a combined approach to the electric and thermal supplies using co-generation or tri-generation biomass plants (with or without solar panels support). A generation plant attached to the factory produces a fraction or the total of the electricity, hot water (or steam) and even cold water that the factory requires for its operation. These proposals represent a deep impact on the existing manufacturing systems. The installation of the required attached infrastructures and their interconnection with the production systems is a complex issue. In some cases, the implementation of these new systems will require changes in the production planning and management.
In next posts, we will talk about the advantages and the obstaclerace that a factory will have to overcome if it decides to become “green”, or at least, to try.
One of those technologies allows machines to discover by themselves the different states an industrial process features. Imagine a computer repeatedly fed with values generated by the sensors installed in an industrial process. Non-supervised machine learning techniques make possible the computer finds out the sensor data belong to, let’s say, three classes and moreover it characterises the classes. What the computer could not do is to name the classes, unless a human operator provides it a clue. That is what the operator does when he examines the computer outcome and assigns the names starting, stopping and running, just to follow the example. But in spite of this limitation, the non-supervised machine learning can be successfully used to detect faults or malfunctions that have never been observed in the past. This is what CARTIF did in the hydroelectric sets of a hydroelectric power station.
Hydroelectric sets are at the heart of hydroelectric power stations. Its role is to transform the energy stored in the mass of water retained by a dam into electric power. Each set is monitored and hundreds of variables are registered: electric current and voltage, temperature measured in the mechanical elements, in the refrigeration and water streams used for refrigeration, flows of water and air, etc. In our case, we have the values recorded along two years during which no fault was detected, and so we had not information about the possible faults. The challenge was to design an algorithm able to detect faults.
The solution developed by CARTIF is based on the SOM (Self-Organising Map) neural network, which is capable of non-supervised learning. The network was fed with all the available data and she was able by herself of discovering the possible states the hydroelectric set could present. The network labels the states in an arbitrary way and to give the correct names a human operator has to collaborate. However, this is not required to detect faults. Since the data used for training represent all the possible non-faulty states, any network input that does not fully fit with those states corresponds to a fault.
This case can be easily identified by checking the similarity between the sensors signals and the prototypes stored by the neural network. When this similarity is too low, it indicates a fault is occurring.
During testing stage, the algorithm implemented by CARTIF was able to detect an overheating twenty minutes before the plant supervision system raised an alarm. It is important to note that our system used already available sensors and no new ones were required.
So, while we wait for the day machines will rule over us, we may use them to implement intelligent algorithms to improve industrial process supervision with no need for high investments.
Firstly, it is necessary to insist on the fact that, talking about scientific and technical progress is to do about economic and social progress. This is like this since homo sapiens appeared on Earth. But it is absolutely obvious since the late Middle Age, when science started to take off and to apply the knowledge of it to know human needs. The Industrial Revolution took its first steps in the eighteenth century after achieving the use and control of natural sources of energy. Then, the scientific and technological progress has continued unabated. Without it, our lives would be much poorer and the welfare state would be an entelechy.
When so many politicians and all kinds of demagogues filled their mouth promising to improve living conditions of citizens, probably they are not thinking that the basis of their claims, if they really want social progress, is linked to scientific and technical progress.
Unfortunately, we have heard some poor references about this only during the last Spanish electoral processes. It must be that it is a subject without influences on the electorate. This is the first and greatest obstacle that society has to lead in order to start to consider essential that the social welfare is closely linked to advances in science and technology. Opinion makers and those who cause these several opinions have a beautiful but difficult task.
The last major financial crisis, which began in 2008, led to the reduction in research and innovation resources. It is true that they were not the only nor, in the short term, the most painful, but with this reduction is impossible to improve in the short and medium term.
It is necessary that those who govern us and who aspire to do, understand the importance of research and innovation, and try to efficiently undertake the necessary and constant actions to be a social value accepted, avoiding the reduction of these resources.. It is necessary to get a suitable atmosphere for the efficient use of public and private resources, in order to be a richer and more solidary society.
Trying to define the ultimate trend is like trying to choose the perfect camera; once you have purchased the brand new toy, your brother-in-law shows up with the double of megapixels. This time we are talking about Industry 4.0, also known as the fourth industrial revolution. This model seeks the deployment of information technologies within the industry with the main objective of creating a seamless interconnection of manufacturing means facilitating the transition towards a Smart Industry (yes, I know, everything seems to be “smarter” nowadays). Putting the things in context, Industry 1.0 and 2.0 are associated to the first mechanic weaving machine and the first mass-production line (Ford-T), respectively.
Wait a minute, maybe the factory where my brother-in-law works doesn’t have the machines interconnected? The answer to the question is not definitive. In a factory (Industry 3.0) with a reasonable automation level, production means are already interconnected. The current technology solutions in automation establish a predefined hierarchy where the connectivity levels (inside a factory) are already predefined, from the sensor that measures the process state, to the software used at the highest levels of decision-making (e.g. for the business planning or logistics). However, Industry 4.0 vision establishes a hyper-connectivitythat goes beyond the factory walls and where production means interact not only with the factory environment itself but along the value chain to which customers, suppliers, logistics etc. belong to.
We, as users, are already used to this hyper-connectivity: commercial offers and marketing-in-context messages arrive at our Smartphone every day, personal and professional information is shared (with our implicit permission) through Internet (google your name and tell me the results). So, which are the benefits for a company if they decide to embrace the Industry 4.0 concept? There are many, to mention a few (apart the well-known competitiveness increase):
A lot of “Likes” (just kidding)
Continuous and collaborative innovation (along the value chain) at product and process (e.g. my supplier innovates in their machinery and this enhances my manufacturing process).
Access to new business models (e.g. personalized products)
A fast reaction and adaptation to changing markets (supply and demand).
The next logical question is if Spanish industry is ready for this 4th revolution. Given the high variety of sectors and companies with different technology maturity levels, the answer is not unique. Something is sure, there are a lot of ready-made technologies: high performance and low cost sensors, embedded systems, data processing and knowledge extraction technologies, encrypting algorithms to name a few. What is missing for Spanish industry then? As with many progresses, these happen faster than we are able to assimilate. We still remember someone close refusing to own a Smartphone. It is the natural man’s resistance (women less) to changes. There is also a training deficit that is already beginning to start mitigating through multidisciplinary programs including robotics, industrial design, programming, etc. However, we cannot wait for these new generations while they get ready (our competitors aren’t waiting). The time is NOW.
The main characters of this revolution are everywhere: big companies as necessities’ generators and main drivers and capital goods manufacturers supplying production machinery for virtually all considered sectors. These capital goods sector have already began a silent “collaborative innovation” process. The lessons learnt from a sector or specific customer, sooner or later, find their way to the next factory. The hyper-connectivitythat Industry 4.0 encourages, seeks to accelerate this collaborative innovation process (not only for the capital goods manufacturers). As an example, machinery embedded intelligence will be able to provide real-time status information (useful for the machinery maker and user) to optimize the process (e.g. energy performance, and maintenance) or to inform OEM’s components suppliers (e.g. reliability information). In addition to the information supplying it is expected that smart machines will be able to automatically influence production process to optimize it.
The key concepts for this revolution are varied and with exotic names (the kind my brother-in-law loves): cyber-physical systems, internet of things or big data to name the most well-known. But this is another history.
